Data Intake
| Index | stub | file | data_type | taxon_string | translation_table |
|---|---|---|---|---|---|
| 0 | KX808498-truncated | KX808498-truncated.gb | GenBank | Caulerpa_cliftonii_HV03798 | 11 |
| 1 | KY509313-truncated | KY509313-truncated.gb | GenBank | Avrainvillea_mazei_HV02664 | 11 |
| 2 | MH591083-truncated | MH591083-truncated.gb | GenBank | Flabellia_petiolata_HV01202 | 11 |
| 3 | MH591084-truncated | MH591084-truncated.gb | GenBank | Flabellia_petiolata_HV01202 | 11 |
| 4 | MH591085-truncated | MH591085-truncated.gb | GenBank | Flabellia_petiolata_HV01202 | 11 |
| 5 | NC_026795-truncated | NC_026795-truncated.txt | GenBank | Bryopsis_plumosa_WEST4718 | 11 |
| 6 | KY819064-truncated-cds | KY819064-truncated.cds.fasta | CDS | Chlorodesmis_fastigiata_HV03865 | 11 |
| 7 | KX808497-truncated | KX808497-truncated.fa | CDS | Derbesia_sp_WEST4838 | 11 |
Orthofinder
| Orthogroup | KX808497-truncated.translated | KX808498-truncated.translated | KY509313-truncated.translated | KY819064-truncated-cds.translated | MH591083-truncated.translated | MH591084-truncated.translated | MH591085-truncated.translated | NC_026795-truncated.translated |
|---|---|---|---|---|---|---|---|---|
| OG0000000 | Derbesia_sp_WEST4838|KX808497-truncated.fa|0|KX808497.1|petA | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|23|petA | Avrainvillea_mazei_HV02664|KY509313-truncated.gb|0|petA | Chlorodesmis_fastigiata_HV03865|KY819064-truncated.cds.fasta|0|KY819064.1|petA | — | Flabellia_petiolata_HV01202|MH591084-truncated.gb|0|petA | — | Bryopsis_plumosa_WEST4718|NC_026795-truncated.txt|0|petA |
| OG0000001 | Derbesia_sp_WEST4838|KX808497-truncated.fa|1|KX808497.1|rpl23 | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|22|rpl23 | Avrainvillea_mazei_HV02664|KY509313-truncated.gb|3|rpl23 | Chlorodesmis_fastigiata_HV03865|KY819064-truncated.cds.fasta|1|KY819064.1|rpl23 | — | Flabellia_petiolata_HV01202|MH591084-truncated.gb|1|rpl23 | — | Bryopsis_plumosa_WEST4718|NC_026795-truncated.txt|1|rpl23 |
| OG0000002 | Derbesia_sp_WEST4838|KX808497-truncated.fa|2|KX808497.1|psaI | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|24|psaI | Avrainvillea_mazei_HV02664|KY509313-truncated.gb|1|psaI | Chlorodesmis_fastigiata_HV03865|KY819064-truncated.cds.fasta|2|KY819064.1|psaI | Flabellia_petiolata_HV01202|MH591083-truncated.gb|1|psaI | — | — | Bryopsis_plumosa_WEST4718|NC_026795-truncated.txt|2|psaI |
| OG0000003 | Derbesia_sp_WEST4838|KX808497-truncated.fa|3|KX808497.1|petG | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|26|petG | Avrainvillea_mazei_HV02664|KY509313-truncated.gb|2|petG | Chlorodesmis_fastigiata_HV03865|KY819064-truncated.cds.fasta|3|KY819064.1|petG | Flabellia_petiolata_HV01202|MH591083-truncated.gb|0|petG | — | — | Bryopsis_plumosa_WEST4718|NC_026795-truncated.txt|3|petG |
| OG0000004 | Derbesia_sp_WEST4838|KX808497-truncated.fa|4|KX808497.1|rbcL | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|27|rbcL | Avrainvillea_mazei_HV02664|KY509313-truncated.gb|4|rbcL | Chlorodesmis_fastigiata_HV03865|KY819064-truncated.cds.fasta|4|KY819064.1|rbcL | Flabellia_petiolata_HV01202|MH591083-truncated.gb|2|rbcL | — | — | Bryopsis_plumosa_WEST4718|NC_026795-truncated.txt|4|rbcL |
| OG0000005 | Derbesia_sp_WEST4838|KX808497-truncated.fa|6|KX808497.1|rps18 | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|29|rps18, Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|30|orf179 | Avrainvillea_mazei_HV02664|KY509313-truncated.gb|5|rps18 | Chlorodesmis_fastigiata_HV03865|KY819064-truncated.cds.fasta|6|KY819064.1|rps18 | — | — | — | Bryopsis_plumosa_WEST4718|NC_026795-truncated.txt|6|rps18 |
| OG0000006 | Derbesia_sp_WEST4838|KX808497-truncated.fa|5|KX808497.1|psbE | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|28|psbE | Avrainvillea_mazei_HV02664|KY509313-truncated.gb|6|psbE | Chlorodesmis_fastigiata_HV03865|KY819064-truncated.cds.fasta|5|KY819064.1|psbE | — | — | — | Bryopsis_plumosa_WEST4718|NC_026795-truncated.txt|5|psbE |
| Orthogroup | KX808497-truncated.translated | KX808498-truncated.translated | KY509313-truncated.translated | KY819064-truncated-cds.translated | MH591083-truncated.translated | MH591084-truncated.translated | MH591085-truncated.translated | NC_026795-truncated.translated |
|---|---|---|---|---|---|---|---|---|
| OG0000007 | — | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|0|rps9 | — | — | — | — | — | — |
| OG0000008 | — | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|1|rpoC1 | — | — | — | — | — | — |
| OG0000009 | — | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|2|rpoC2 | — | — | — | — | — | — |
| OG0000010 | — | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|3|psaB | — | — | — | — | — | — |
| OG0000011 | — | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|4|psbZ | — | — | — | — | — | — |
| OG0000012 | — | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|5|orf180 | — | — | — | — | — | — |
| OG0000013 | — | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|6|orf116 | — | — | — | — | — | — |
| OG0000014 | — | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|7|orf144 | — | — | — | — | — | — |
| OG0000015 | — | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|8|orf519 | — | — | — | — | — | — |
| OG0000016 | — | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|9|psbA | — | — | — | — | — | — |
| OG0000017 | — | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|10|orf128 | — | — | — | — | — | — |
| OG0000018 | — | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|11|rpoA | — | — | — | — | — | — |
| OG0000019 | — | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|12|rps11 | — | — | — | — | — | — |
| OG0000020 | — | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|13|rpl36 | — | — | — | — | — | — |
| OG0000021 | — | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|14|infA | — | — | — | — | — | — |
| OG0000022 | — | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|15|rps8 | — | — | — | — | — | — |
| OG0000023 | — | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|16|rpl5 | — | — | — | — | — | — |
| OG0000024 | — | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|17|rpl14 | — | — | — | — | — | — |
| OG0000025 | — | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|18|rpl16 | — | — | — | — | — | — |
| OG0000026 | — | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|19|rps3 | — | — | — | — | — | — |
| OG0000027 | — | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|20|rps19 | — | — | — | — | — | — |
| OG0000028 | — | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|21|rpl2 | — | — | — | — | — | — |
| OG0000029 | — | Caulerpa_cliftonii_HV03798|KX808498-truncated.gb|25|ycf20 | — | — | — | — | — | — |
| OG0000030 | — | — | — | — | — | — | Flabellia_petiolata_HV01202|MH591085-truncated.gb|0|psbE | — |
| Input | KX808497-truncated.translated | KX808498-truncated.translated | KY509313-truncated.translated | KY819064-truncated-cds.translated | MH591083-truncated.translated | MH591084-truncated.translated | MH591085-truncated.translated | NC_026795-truncated.translated |
|---|---|---|---|---|---|---|---|---|
| KX808497-truncated.translated | 7 | 7 | 7 | 7 | 3 | 2 | 0 | 7 |
| KX808498-truncated.translated | 7 | 7 | 7 | 7 | 3 | 2 | 0 | 7 |
| KY509313-truncated.translated | 7 | 7 | 7 | 7 | 3 | 2 | 0 | 7 |
| KY819064-truncated-cds.translated | 7 | 7 | 7 | 7 | 3 | 2 | 0 | 7 |
| MH591083-truncated.translated | 3 | 3 | 3 | 3 | 3 | 0 | 0 | 3 |
| MH591084-truncated.translated | 2 | 2 | 2 | 2 | 0 | 2 | 0 | 2 |
| MH591085-truncated.translated | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| NC_026795-truncated.translated | 7 | 7 | 7 | 7 | 3 | 2 | 0 | 7 |
results/orthofinder/output/Orthogroup_Sequences/OG0000000.fa results/orthofinder/output/Orthogroup_Sequences/OG0000001.fa results/orthofinder/output/Orthogroup_Sequences/OG0000002.fa results/orthofinder/output/Orthogroup_Sequences/OG0000003.fa results/orthofinder/output/Orthogroup_Sequences/OG0000004.fa results/orthofinder/output/Orthogroup_Sequences/OG0000006.fa
results/orthofinder/orthosnap/OG0000005/OG0000005_orthosnap_0.fa
Alignment
results/alignment/trimmed_protein/OG0000004.trimmed.protein.alignment.fa results/alignment/trimmed_protein/OG0000000.trimmed.protein.alignment.fa
Supermatrix
General Characteristics ======================= 6 Number of taxa 796 Alignment length 100 Parsimony informative sites 100 Variable sites 685 Constant sites Character Frequencies ===================== Y 176 W 52 V 338 T 242 S 200 R 217 Q 230 P 268 N 222 M 77 L 412 K 301 I 309 H 97 G 410 F 230 E 271 D 231 C 79 A 366 - 48
IQ-TREE 2.2.0.3 COVID-edition built Aug 2 2022
Input file name: results/supermatrix/supermatrix.protein.fa
Type of analysis: ModelFinder + tree reconstruction + ultrafast bootstrap (1000 replicates)
Random seed number: 413450
REFERENCES
----------
To cite IQ-TREE please use:
Bui Quang Minh, Heiko A. Schmidt, Olga Chernomor, Dominik Schrempf,
Michael D. Woodhams, Arndt von Haeseler, and Robert Lanfear (2020)
IQ-TREE 2: New models and efficient methods for phylogenetic inference
in the genomic era. Mol. Biol. Evol., in press.
https://doi.org/10.1093/molbev/msaa015
To cite ModelFinder please use:
Subha Kalyaanamoorthy, Bui Quang Minh, Thomas KF Wong, Arndt von Haeseler,
and Lars S Jermiin (2017) ModelFinder: Fast model selection for
accurate phylogenetic estimates. Nature Methods, 14:587–589.
https://doi.org/10.1038/nmeth.4285
Since you used ultrafast bootstrap (UFBoot) please also cite:
Diep Thi Hoang, Olga Chernomor, Arndt von Haeseler, Bui Quang Minh,
and Le Sy Vinh (2018) UFBoot2: Improving the ultrafast bootstrap
approximation. Mol. Biol. Evol., 35:518–522.
https://doi.org/10.1093/molbev/msx281
SEQUENCE ALIGNMENT
------------------
Input data: 6 sequences with 796 amino-acid sites
Number of constant sites: 553 (= 69.4724% of all sites)
Number of invariant (constant or ambiguous constant) sites: 553 (= 69.4724% of all sites)
Number of parsimony informative sites: 100
Number of distinct site patterns: 255
ModelFinder
-----------
Best-fit model according to BIC: cpREV+I+G4
List of models sorted by BIC scores:
Model LogL AIC w-AIC AICc w-AICc BIC w-BIC
cpREV+I+G4 -4754.045 9530.090 + 0.864 9530.427 + 0.904 9581.566 + 0.563
cpREV+G4 -4757.639 9535.278 + 0.0645 9535.558 + 0.0695 9582.074 + 0.437
LG+I+G4 -4762.923 9547.846 - 0.00012 9548.183 - 0.000126 9599.322 - 7.85e-05
LG+G4 -4769.787 9559.575 - 3.42e-07 9559.855 - 3.68e-07 9606.371 - 2.31e-06
Q.pfam+I+G4 -4772.958 9567.915 - 5.28e-09 9568.252 - 5.53e-09 9619.391 - 3.44e-09
Q.pfam+G4 -4779.840 9579.680 - 1.47e-11 9579.960 - 1.59e-11 9626.476 - 9.96e-11
WAG+I+G4 -4778.199 9578.397 - 2.8e-11 9578.734 - 2.93e-11 9629.873 - 1.82e-11
WAG+G4 -4782.001 9584.002 - 1.7e-12 9584.283 - 1.83e-12 9630.798 - 1.15e-11
VT+G4 -4783.028 9586.055 - 6.08e-13 9586.335 - 6.54e-13 9632.851 - 4.11e-12
VT+I+G4 -4779.908 9581.816 - 5.06e-12 9582.153 - 5.3e-12 9633.291 - 3.3e-12
LG+I -4785.119 9590.237 - 7.51e-14 9590.517 - 8.08e-14 9637.033 - 5.08e-13
Q.plant+I+G4 -4784.005 9590.009 - 8.41e-14 9590.346 - 8.81e-14 9641.485 - 5.49e-14
Q.yeast+I+G4 -4784.243 9590.486 - 6.63e-14 9590.823 - 6.94e-14 9641.962 - 4.32e-14
rtREV+I+G4 -4785.918 9593.835 - 1.24e-14 9594.172 - 1.3e-14 9645.311 - 8.1e-15
rtREV+G4 -4791.371 9602.742 - 1.45e-16 9603.022 - 1.56e-16 9649.538 - 9.79e-16
JTTDCMut+I+G4 -4789.487 9600.973 - 3.5e-16 9601.310 - 3.66e-16 9652.449 - 2.28e-16
Q.yeast+G4 -4793.400 9606.800 - 1.9e-17 9607.080 - 2.05e-17 9653.596 - 1.29e-16
Q.plant+G4 -4794.205 9608.410 - 8.5e-18 9608.691 - 9.15e-18 9655.206 - 5.75e-17
JTT+I+G4 -4791.124 9604.249 - 6.81e-17 9604.585 - 7.12e-17 9655.724 - 4.44e-17
JTTDCMut+G4 -4795.768 9611.536 - 1.78e-18 9611.816 - 1.92e-18 9658.332 - 1.2e-17
JTT+G4 -4797.364 9614.729 - 3.61e-19 9615.009 - 3.88e-19 9661.525 - 2.44e-18
Q.insect+I+G4 -4795.237 9612.475 - 1.11e-18 9612.811 - 1.17e-18 9663.950 - 7.26e-19
Blosum62+G4 -4799.131 9618.261 - 6.17e-20 9618.542 - 6.64e-20 9665.057 - 4.17e-19
Blosum62+I+G4 -4796.305 9614.610 - 3.83e-19 9614.947 - 4.01e-19 9666.086 - 2.5e-19
PMB+G4 -4804.054 9628.108 - 4.49e-22 9628.388 - 4.83e-22 9674.904 - 3.04e-21
Q.insect+G4 -4804.135 9628.271 - 4.14e-22 9628.551 - 4.45e-22 9675.067 - 2.8e-21
cpREV+F+I+G4 -4737.617 9535.233 + 0.066 9537.665 - 0.0242 9675.621 - 2.12e-21
cpREV+F+G4 -4741.076 9540.152 - 0.00564 9542.424 - 0.00224 9675.861 - 1.88e-21
PMB+I+G4 -4801.637 9625.275 - 1.85e-21 9625.612 - 1.94e-21 9676.750 - 1.21e-21
mtART+F+I+G4 -4744.357 9548.714 - 7.8e-05 9551.145 - 2.87e-05 9689.102 - 2.51e-24
mtZOA+F+I+G4 -4745.935 9551.870 - 1.61e-05 9554.301 - 5.91e-06 9692.258 - 5.18e-25
LG+F+I+G4 -4746.272 9552.545 - 1.15e-05 9554.976 - 4.22e-06 9692.933 - 3.69e-25
Dayhoff+I+G4 -4810.213 9642.426 - 3.49e-25 9642.763 - 3.65e-25 9693.902 - 2.28e-25
DCMut+I+G4 -4810.333 9642.665 - 3.1e-25 9643.002 - 3.24e-25 9694.141 - 2.02e-25
Dayhoff+G4 -4815.703 9651.406 - 3.92e-27 9651.687 - 4.22e-27 9698.202 - 2.65e-26
DCMut+G4 -4815.774 9651.549 - 3.65e-27 9651.829 - 3.93e-27 9698.345 - 2.47e-26
mtART+F+G4 -4752.459 9562.918 - 6.43e-08 9565.189 - 2.56e-08 9698.626 - 2.14e-26
LG+F+G4 -4752.616 9563.233 - 5.49e-08 9565.505 - 2.18e-08 9698.941 - 1.83e-26
mtZOA+F+G4 -4754.209 9566.418 - 1.12e-08 9568.689 - 4.44e-09 9702.126 - 3.73e-27
Q.pfam+F+I+G4 -4752.279 9564.559 - 2.83e-08 9566.990 - 1.04e-08 9704.947 - 9.09e-28
mtInv+F+I+G4 -4753.769 9567.537 - 6.38e-09 9569.969 - 2.34e-09 9707.925 - 2.05e-28
WAG+F+I+G4 -4753.892 9567.784 - 5.64e-09 9570.216 - 2.07e-09 9708.172 - 1.81e-28
WAG+F+G4 -4757.613 9573.227 - 3.71e-10 9575.498 - 1.48e-10 9708.935 - 1.24e-28
Q.pfam+F+G4 -4758.639 9575.278 - 1.33e-10 9577.550 - 5.29e-11 9710.987 - 4.44e-29
rtREV+F+I+G4 -4755.824 9571.649 - 8.16e-10 9574.080 - 3e-10 9712.037 - 2.62e-29
mtInv+F+G4 -4760.247 9578.495 - 2.66e-11 9580.766 - 1.06e-11 9714.203 - 8.89e-30
rtREV+F+G4 -4761.112 9580.224 - 1.12e-11 9582.496 - 4.46e-12 9715.933 - 3.74e-30
Q.yeast+F+I+G4 -4760.383 9580.766 - 8.55e-12 9583.198 - 3.14e-12 9721.154 - 2.75e-31
Q.plant+F+I+G4 -4762.682 9585.365 - 8.58e-13 9587.796 - 3.15e-13 9725.752 - 2.76e-32
mtREV+F+I+G4 -4763.806 9587.612 - 2.79e-13 9590.043 - 1.02e-13 9728.000 - 8.97e-33
Q.yeast+F+G4 -4768.290 9594.579 - 8.56e-15 9596.851 - 3.41e-15 9730.288 - 2.86e-33
mtREV+F+G4 -4770.353 9598.705 - 1.09e-15 9600.977 - 4.33e-16 9734.414 - 3.63e-34
VT+F+G4 -4771.629 9601.259 - 3.04e-16 9603.530 - 1.21e-16 9736.967 - 1.01e-34
Q.plant+F+G4 -4771.916 9601.831 - 2.28e-16 9604.103 - 9.07e-17 9737.540 - 7.61e-35
VT+F+I+G4 -4768.882 9597.764 - 1.74e-15 9600.196 - 6.4e-16 9738.152 - 5.6e-35
Q.insect+F+I+G4 -4768.958 9597.915 - 1.62e-15 9600.347 - 5.93e-16 9738.303 - 5.19e-35
Dayhoff+F+I+G4 -4769.817 9599.634 - 6.84e-16 9602.066 - 2.51e-16 9740.022 - 2.2e-35
DCMut+F+I+G4 -4769.947 9599.894 - 6.01e-16 9602.325 - 2.21e-16 9740.282 - 1.93e-35
JTTDCMut+F+I+G4 -4771.470 9602.940 - 1.31e-16 9605.371 - 4.81e-17 9743.328 - 4.21e-36
Dayhoff+F+G4 -4775.697 9609.395 - 5.19e-18 9611.666 - 2.07e-18 9745.103 - 1.73e-36
DCMut+F+G4 -4775.776 9609.552 - 4.8e-18 9611.823 - 1.91e-18 9745.260 - 1.6e-36
JTT+F+I+G4 -4773.051 9606.102 - 2.69e-17 9608.534 - 9.9e-18 9746.490 - 8.66e-37
Q.insect+F+G4 -4776.671 9611.342 - 1.96e-18 9613.614 - 7.8e-19 9747.050 - 6.55e-37
JTTDCMut+F+G4 -4777.486 9612.972 - 8.68e-19 9615.244 - 3.45e-19 9748.681 - 2.9e-37
HIVb+I+G4 -4838.442 9698.885 - 1.92e-37 9699.221 - 2.01e-37 9750.360 - 1.25e-37
PMB+F+G4 -4778.450 9614.900 - 3.31e-19 9617.172 - 1.32e-19 9750.609 - 1.11e-37
mtMet+F+I+G4 -4775.135 9610.270 - 3.35e-18 9612.702 - 1.23e-18 9750.658 - 1.08e-37
JTT+F+G4 -4778.998 9615.995 - 1.92e-19 9618.267 - 7.62e-20 9751.704 - 6.39e-38
PMB+F+I+G4 -4776.222 9612.445 - 1.13e-18 9614.876 - 4.15e-19 9752.833 - 3.63e-38
mtMet+F+G4 -4783.430 9624.859 - 2.28e-21 9627.131 - 9.06e-22 9760.568 - 7.6e-40
HIVb+G4 -4847.221 9714.442 - 8.03e-41 9714.722 - 8.65e-41 9761.238 - 5.44e-40
Blosum62+F+G4 -4784.806 9627.612 - 5.75e-22 9629.884 - 2.29e-22 9763.321 - 1.92e-40
Blosum62+F+I+G4 -4782.259 9624.519 - 2.7e-21 9626.950 - 9.92e-22 9764.907 - 8.68e-41
FLU+I+G4 -4845.984 9713.969 - 1.02e-40 9714.305 - 1.07e-40 9765.444 - 6.64e-41
Q.mammal+I+G4 -4850.226 9722.451 - 1.46e-42 9722.788 - 1.53e-42 9773.927 - 9.55e-43
Q.bird+I+G4 -4851.171 9724.342 - 5.69e-43 9724.679 - 5.95e-43 9775.818 - 3.71e-43
FLU+G4 -4856.287 9732.574 - 9.28e-45 9732.854 - 9.99e-45 9779.370 - 6.28e-44
Q.mammal+G4 -4860.506 9741.012 - 1.36e-46 9741.293 - 1.47e-46 9787.808 - 9.24e-46
Q.bird+G4 -4863.081 9746.163 - 1.04e-47 9746.443 - 1.12e-47 9792.959 - 7.03e-47
FLU+F+I+G4 -4803.431 9666.863 - 1.72e-30 9669.294 - 6.33e-31 9807.251 - 5.54e-50
mtMAM+F+I+G4 -4805.644 9671.288 - 1.89e-31 9673.719 - 6.93e-32 9811.676 - 6.07e-51
HIVb+F+I+G4 -4806.965 9673.931 - 5.03e-32 9676.362 - 1.85e-32 9814.319 - 1.62e-51
mtVer+F+I+G4 -4808.228 9676.457 - 1.42e-32 9678.888 - 5.23e-33 9816.845 - 4.58e-52
Q.mammal+F+I+G4 -4808.873 9677.746 - 7.47e-33 9680.178 - 2.74e-33 9818.134 - 2.4e-52
FLU+F+G4 -4812.951 9683.903 - 3.44e-34 9686.174 - 1.37e-34 9819.611 - 1.15e-52
HIVb+F+G4 -4815.656 9689.312 - 2.3e-35 9691.583 - 9.15e-36 9825.020 - 7.68e-54
mtMAM+F+G4 -4816.650 9691.300 - 8.51e-36 9693.572 - 3.39e-36 9827.009 - 2.84e-54
Q.mammal+F+G4 -4818.621 9695.242 - 1.19e-36 9697.514 - 4.72e-37 9830.951 - 3.96e-55
mtVer+F+G4 -4821.298 9700.595 - 8.16e-38 9702.867 - 3.25e-38 9836.303 - 2.72e-56
Q.bird+F+I+G4 -4818.887 9697.773 - 3.35e-37 9700.205 - 1.23e-37 9838.161 - 1.08e-56
Q.bird+F+G4 -4830.313 9718.627 - 9.91e-42 9720.898 - 3.94e-42 9854.335 - 3.31e-60
FLAVI+I+G4 -4909.651 9841.303 - 2.28e-68 9841.639 - 2.38e-68 9892.778 - 1.48e-68
FLAVI+F+I+G4 -4846.739 9753.479 - 2.68e-49 9755.910 - 9.84e-50 9893.867 - 8.62e-69
mtZOA+I+G4 -4912.946 9847.893 - 8.44e-70 9848.230 - 8.83e-70 9899.369 - 5.5e-70
mtZOA+G4 -4920.530 9861.060 - 1.17e-72 9861.341 - 1.26e-72 9907.856 - 7.9e-72
FLAVI+G4 -4921.129 9862.258 - 6.41e-73 9862.538 - 6.9e-73 9909.054 - 4.34e-72
FLAVI+F+G4 -4859.578 9777.156 - 1.93e-54 9779.428 - 7.69e-55 9912.865 - 6.46e-73
mtART+I+G4 -4936.533 9895.066 - 4.82e-80 9895.402 - 5.04e-80 9946.541 - 3.14e-80
mtART+G4 -4943.775 9907.550 - 9.38e-83 9907.830 - 1.01e-82 9954.346 - 6.35e-82
LG -4967.424 9952.849 - 1.37e-92 9953.078 - 1.51e-92 9994.965 - 9.59e-91
HIVw+I+G4 -4961.731 9945.463 - 5.49e-91 9945.799 - 5.74e-91 9996.938 - 3.58e-91
mtREV+I+G4 -4965.210 9952.420 - 1.69e-92 9952.756 - 1.77e-92 10003.895 - 1.1e-92
mtREV+G4 -4973.869 9967.739 - 7.98e-96 9968.019 - 8.59e-96 10014.535 - 5.4e-95
HIVw+F+I+G4 -4907.718 9875.435 - 8.82e-76 9877.867 - 3.24e-76 10015.823 - 2.84e-95
mtMet+I+G4 -4971.840 9965.680 - 2.23e-95 9966.017 - 2.34e-95 10017.156 - 1.46e-95
HIVw+G4 -4977.561 9975.122 - 1.99e-97 9975.402 - 2.14e-97 10021.918 - 1.35e-96
mtMet+G4 -4981.233 9982.467 - 5.06e-99 9982.747 - 5.45e-99 10029.263 - 3.42e-98
HIVw+F+G4 -4922.673 9903.345 - 7.67e-82 9905.617 - 3.05e-82 10039.054 - 2.56e-100
mtMAM+I+G4 -5003.477 10028.953 - 4.07e-109 10029.290 - 4.26e-109 10080.429 - 2.65e-109
mtInv+I+G4 -5006.654 10035.307 - 1.7e-110 10035.644 - 1.78e-110 10086.783 - 1.11e-110
mtMAM+G4 -5012.493 10044.986 - 1.34e-112 10045.266 - 1.45e-112 10091.782 - 9.09e-112
mtVer+I+G4 -5009.382 10040.765 - 1.11e-111 10041.102 - 1.16e-111 10092.240 - 7.23e-112
mtInv+G4 -5015.079 10050.159 - 1.01e-113 10050.439 - 1.09e-113 10096.955 - 6.84e-113
mtVer+G4 -5020.644 10061.287 - 3.88e-116 10061.567 - 4.17e-116 10108.083 - 2.62e-115
AIC, w-AIC : Akaike information criterion scores and weights.
AICc, w-AICc : Corrected AIC scores and weights.
BIC, w-BIC : Bayesian information criterion scores and weights.
Plus signs denote the 95% confidence sets.
Minus signs denote significant exclusion.
SUBSTITUTION PROCESS
--------------------
Model of substitution: cpREV+I+G4
State frequencies: (model)
Model of rate heterogeneity: Invar+Gamma with 4 categories
Proportion of invariable sites: 0.4626
Gamma shape alpha: 0.7873
Category Relative_rate Proportion
0 0 0.4626
1 0.1728 0.1344
2 0.7480 0.1344
3 1.7740 0.1344
4 4.7484 0.1344
Relative rates are computed as MEAN of the portion of the Gamma distribution falling in the category.
MAXIMUM LIKELIHOOD TREE
-----------------------
Log-likelihood of the tree: -4753.6149 (s.e. 129.2064)
Unconstrained log-likelihood (without tree): -3410.0651
Number of free parameters (#branches + #model parameters): 11
Akaike information criterion (AIC) score: 9529.2298
Corrected Akaike information criterion (AICc) score: 9529.5665
Bayesian information criterion (BIC) score: 9580.7054
Total tree length (sum of branch lengths): 0.8705
Sum of internal branch lengths: 0.2131 (24.4767% of tree length)
NOTE: Tree is UNROOTED although outgroup taxon 'Avrainvillea_mazei_HV02664' is drawn at root
Numbers in parentheses are ultrafast bootstrap support (%)
+---------------------------------------------Avrainvillea_mazei_HV02664
|
| +----------------------Bryopsis_plumosa_WEST4718
+-----------------| (98)
| +-------------------------------------Derbesia_sp_WEST4838
|
| +---------------------------------Caulerpa_cliftonii_HV03798
+------------------------| (100)
| +---------------------Chlorodesmis_fastigiata_HV03865
+----------| (92)
+-----Flabellia_petiolata_HV01202
Tree in newick format:
(Avrainvillea_mazei_HV02664:0.1774010564,(Bryopsis_plumosa_WEST4718:0.0911761379,Derbesia_sp_WEST4838:0.1476272926)98:0.0699127956,(Caulerpa_cliftonii_HV03798:0.1301703118,(Chlorodesmis_fastigiata_HV03865:0.0865187215,Flabellia_petiolata_HV01202:0.0245104903)92:0.0442681486)100:0.0988799497);
CONSENSUS TREE
--------------
Consensus tree is constructed from 1000 bootstrap trees
Log-likelihood of consensus tree: -4753.614908
Robinson-Foulds distance between ML tree and consensus tree: 0
Branches with support >0.000000% are kept (extended consensus)
Branch lengths are optimized by maximum likelihood on original alignment
Numbers in parentheses are bootstrap supports (%)
+---------------------------------------------Avrainvillea_mazei_HV02664
|
| +----------------------Bryopsis_plumosa_WEST4718
+-----------------| (98)
| +-------------------------------------Derbesia_sp_WEST4838
|
| +---------------------------------Caulerpa_cliftonii_HV03798
+------------------------| (100)
| +---------------------Chlorodesmis_fastigiata_HV03865
+----------| (92)
+-----Flabellia_petiolata_HV01202
Consensus tree in newick format:
(Avrainvillea_mazei_HV02664:0.1774144310,(Bryopsis_plumosa_WEST4718:0.0911750125,Derbesia_sp_WEST4838:0.1476344869)98:0.0699199323,(Caulerpa_cliftonii_HV03798:0.1302020010,(Chlorodesmis_fastigiata_HV03865:0.0865481861,Flabellia_petiolata_HV01202:0.0244952561)92:0.0442550027)100:0.0988875469);
TIME STAMP
----------
Date and time: Sat Dec 16 03:42:26 2023
Total CPU time used: 956.280323 seconds (0h:15m:56s)
Total wall-clock time used: 74.05670908 seconds (0h:1m:14s)
IQ-TREE multicore version 2.2.0.3 COVID-edition for Linux 64-bit built Aug 2 2022
Developed by Bui Quang Minh, James Barbetti, Nguyen Lam Tung,
Olga Chernomor, Heiko Schmidt, Dominik Schrempf, Michael Woodhams, Ly Trong Nhan.
Host: herbairumphylo (AVX512, FMA3, 283 GB RAM)
Command: iqtree2 -s results/supermatrix/supermatrix.protein.fa -bb 1000 -m TEST -nt 32 -redo
Seed: 413450 (Using SPRNG - Scalable Parallel Random Number Generator)
Time: Sat Dec 16 03:40:29 2023
Kernel: AVX+FMA - 32 threads (32 CPU cores detected)
Reading alignment file results/supermatrix/supermatrix.protein.fa ... Fasta format detected
Reading fasta file: done in 0.000102289 secs
Alignment most likely contains protein sequences
Constructing alignment: done in 0.0012522 secs using 291.5% CPU
Alignment has 6 sequences with 796 columns, 255 distinct patterns
100 parsimony-informative, 143 singleton sites, 553 constant sites
Gap/Ambiguity Composition p-value
Analyzing sequences: done in 0.00294819 secs using 4133% CPU
1 Avrainvillea_mazei_HV02664 1.13% passed 99.95%
2 Bryopsis_plumosa_WEST4718 0.38% passed 100.00%
3 Caulerpa_cliftonii_HV03798 0.00% passed 100.00%
4 Chlorodesmis_fastigiata_HV03865 1.51% passed 100.00%
5 Derbesia_sp_WEST4838 0.63% passed 100.00%
6 Flabellia_petiolata_HV01202 2.39% passed 100.00%
**** TOTAL 1.01% 0 sequences failed composition chi2 test (p-value<5%; df=19)
Checking for duplicate sequences: done in 0.00191325 secs using 624.9% CPU
WARNING: 32 threads for alignment length 255 will slow down analysis
Create initial parsimony tree by phylogenetic likelihood library (PLL)... 0.000 seconds
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
Perform fast likelihood tree search using LG+I+G model...
Estimate model parameters (epsilon = 5.000)
Perform nearest neighbor interchange...
Optimizing NNI: done in 0.00980802 secs using 2466% CPU
Estimate model parameters (epsilon = 1.000)
1. Initial log-likelihood: -4763.107
Optimal log-likelihood: -4763.040
Proportion of invariable sites: 0.357
Gamma shape alpha: 0.456
Parameters optimization took 1 rounds (0.008 sec)
Time for fast ML tree search: 0.055 seconds
NOTE: ModelFinder requires 2 MB RAM!
ModelFinder will test up to 224 protein models (sample size: 796) ...
No. Model -LnL df AIC AICc BIC
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
1 LG 4967.424 9 9952.849 9953.078 9994.965
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
2 LG+I 4785.119 10 9590.237 9590.517 9637.033
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
3 LG+G4 4769.787 10 9559.575 9559.855 9606.371
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
4 LG+I+G4 4762.923 11 9547.846 9548.183 9599.322
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
7 LG+F+G4 4752.616 29 9563.233 9565.505 9698.941
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
8 LG+F+I+G4 4746.272 30 9552.545 9554.976 9692.933
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
11 WAG+G4 4782.001 10 9584.002 9584.283 9630.798
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
12 WAG+I+G4 4778.199 11 9578.397 9578.734 9629.873
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
15 WAG+F+G4 4757.613 29 9573.227 9575.498 9708.935
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
16 WAG+F+I+G4 4753.892 30 9567.784 9570.216 9708.172
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
19 JTT+G4 4797.364 10 9614.729 9615.009 9661.525
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
20 JTT+I+G4 4791.124 11 9604.249 9604.585 9655.724
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
23 JTT+F+G4 4778.998 29 9615.995 9618.267 9751.704
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
24 JTT+F+I+G4 4773.051 30 9606.102 9608.534 9746.490
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
27 Q.pfam+G4 4779.840 10 9579.680 9579.960 9626.476
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
28 Q.pfam+I+G4 4772.958 11 9567.915 9568.252 9619.391
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
31 Q.pfam+F+G4 4758.639 29 9575.278 9577.550 9710.987
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
32 Q.pfam+F+I+G4 4752.279 30 9564.559 9566.990 9704.947
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
35 Q.bird+G4 4863.081 10 9746.163 9746.443 9792.959
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
36 Q.bird+I+G4 4851.171 11 9724.342 9724.679 9775.818
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
39 Q.bird+F+G4 4830.313 29 9718.627 9720.898 9854.335
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
40 Q.bird+F+I+G4 4818.887 30 9697.773 9700.205 9838.161
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
43 Q.mammal+G4 4860.506 10 9741.012 9741.293 9787.808
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
44 Q.mammal+I+G4 4850.226 11 9722.451 9722.788 9773.927
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
47 Q.mammal+F+G4 4818.621 29 9695.242 9697.514 9830.951
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
48 Q.mammal+F+I+G4 4808.873 30 9677.746 9680.178 9818.134
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
51 Q.insect+G4 4804.135 10 9628.271 9628.551 9675.067
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
52 Q.insect+I+G4 4795.237 11 9612.475 9612.811 9663.950
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
55 Q.insect+F+G4 4776.671 29 9611.342 9613.614 9747.050
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
56 Q.insect+F+I+G4 4768.958 30 9597.915 9600.347 9738.303
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
59 Q.plant+G4 4794.205 10 9608.410 9608.691 9655.206
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
60 Q.plant+I+G4 4784.005 11 9590.009 9590.346 9641.485
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
63 Q.plant+F+G4 4771.916 29 9601.831 9604.103 9737.540
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
64 Q.plant+F+I+G4 4762.682 30 9585.365 9587.796 9725.752
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
67 Q.yeast+G4 4793.400 10 9606.800 9607.080 9653.596
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
68 Q.yeast+I+G4 4784.243 11 9590.486 9590.823 9641.962
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
71 Q.yeast+F+G4 4768.290 29 9594.579 9596.851 9730.288
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
72 Q.yeast+F+I+G4 4760.383 30 9580.766 9583.198 9721.154
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
75 JTTDCMut+G4 4795.768 10 9611.536 9611.816 9658.332
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
76 JTTDCMut+I+G4 4789.487 11 9600.973 9601.310 9652.449
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
79 JTTDCMut+F+G4 4777.486 29 9612.972 9615.244 9748.681
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
80 JTTDCMut+F+I+G4 4771.470 30 9602.940 9605.371 9743.328
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
83 DCMut+G4 4815.774 10 9651.549 9651.829 9698.345
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
84 DCMut+I+G4 4810.333 11 9642.665 9643.002 9694.141
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
87 DCMut+F+G4 4775.776 29 9609.552 9611.823 9745.260
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
88 DCMut+F+I+G4 4769.947 30 9599.894 9602.325 9740.282
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
91 VT+G4 4783.028 10 9586.055 9586.335 9632.851
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
92 VT+I+G4 4779.908 11 9581.816 9582.153 9633.291
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
95 VT+F+G4 4771.629 29 9601.259 9603.530 9736.967
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
96 VT+F+I+G4 4768.882 30 9597.764 9600.196 9738.152
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
99 PMB+G4 4804.054 10 9628.108 9628.388 9674.904
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
100 PMB+I+G4 4801.637 11 9625.275 9625.612 9676.750
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
103 PMB+F+G4 4778.450 29 9614.900 9617.172 9750.609
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
104 PMB+F+I+G4 4776.222 30 9612.445 9614.876 9752.833
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
107 Blosum62+G4 4799.131 10 9618.261 9618.542 9665.057
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
108 Blosum62+I+G4 4796.305 11 9614.610 9614.947 9666.086
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
111 Blosum62+F+G4 4784.806 29 9627.612 9629.884 9763.321
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
112 Blosum62+F+I+G4 4782.259 30 9624.519 9626.950 9764.907
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
115 Dayhoff+G4 4815.703 10 9651.406 9651.687 9698.202
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
116 Dayhoff+I+G4 4810.213 11 9642.426 9642.763 9693.902
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
119 Dayhoff+F+G4 4775.697 29 9609.395 9611.666 9745.103
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
120 Dayhoff+F+I+G4 4769.817 30 9599.634 9602.066 9740.022
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
123 mtREV+G4 4973.869 10 9967.739 9968.019 10014.535
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
124 mtREV+I+G4 4965.210 11 9952.420 9952.756 10003.895
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
127 mtREV+F+G4 4770.353 29 9598.705 9600.977 9734.414
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
128 mtREV+F+I+G4 4763.806 30 9587.612 9590.043 9728.000
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
131 mtART+G4 4943.775 10 9907.550 9907.830 9954.346
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
132 mtART+I+G4 4936.533 11 9895.066 9895.402 9946.541
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
135 mtART+F+G4 4752.459 29 9562.918 9565.189 9698.626
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
136 mtART+F+I+G4 4744.357 30 9548.714 9551.145 9689.102
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
139 mtZOA+G4 4920.530 10 9861.060 9861.341 9907.856
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
140 mtZOA+I+G4 4912.946 11 9847.893 9848.230 9899.369
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
143 mtZOA+F+G4 4754.209 29 9566.418 9568.689 9702.126
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
144 mtZOA+F+I+G4 4745.935 30 9551.870 9554.301 9692.258
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
147 mtMet+G4 4981.233 10 9982.467 9982.747 10029.263
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
148 mtMet+I+G4 4971.840 11 9965.680 9966.017 10017.156
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
151 mtMet+F+G4 4783.430 29 9624.859 9627.131 9760.568
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
152 mtMet+F+I+G4 4775.135 30 9610.270 9612.702 9750.658
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
155 mtVer+G4 5020.644 10 10061.287 10061.567 10108.083
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
156 mtVer+I+G4 5009.382 11 10040.765 10041.102 10092.240
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
159 mtVer+F+G4 4821.298 29 9700.595 9702.867 9836.303
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
160 mtVer+F+I+G4 4808.228 30 9676.457 9678.888 9816.845
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
163 mtInv+G4 5015.079 10 10050.159 10050.439 10096.955
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
164 mtInv+I+G4 5006.654 11 10035.307 10035.644 10086.783
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
167 mtInv+F+G4 4760.247 29 9578.495 9580.766 9714.203
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
168 mtInv+F+I+G4 4753.769 30 9567.537 9569.969 9707.925
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
171 mtMAM+G4 5012.493 10 10044.986 10045.266 10091.782
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
172 mtMAM+I+G4 5003.477 11 10028.953 10029.290 10080.429
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
175 mtMAM+F+G4 4816.650 29 9691.300 9693.572 9827.009
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
176 mtMAM+F+I+G4 4805.644 30 9671.288 9673.719 9811.676
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
179 FLAVI+G4 4921.129 10 9862.258 9862.538 9909.054
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
180 FLAVI+I+G4 4909.651 11 9841.303 9841.639 9892.778
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
183 FLAVI+F+G4 4859.578 29 9777.156 9779.428 9912.865
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
184 FLAVI+F+I+G4 4846.739 30 9753.479 9755.910 9893.867
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
187 HIVb+G4 4847.221 10 9714.442 9714.722 9761.238
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
188 HIVb+I+G4 4838.442 11 9698.885 9699.221 9750.360
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
191 HIVb+F+G4 4815.656 29 9689.312 9691.583 9825.020
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
192 HIVb+F+I+G4 4806.965 30 9673.931 9676.362 9814.319
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
195 HIVw+G4 4977.561 10 9975.122 9975.402 10021.918
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
196 HIVw+I+G4 4961.731 11 9945.463 9945.799 9996.938
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
199 HIVw+F+G4 4922.673 29 9903.345 9905.617 10039.054
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
200 HIVw+F+I+G4 4907.718 30 9875.435 9877.867 10015.823
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
203 FLU+G4 4856.287 10 9732.574 9732.854 9779.370
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
204 FLU+I+G4 4845.984 11 9713.969 9714.305 9765.444
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
207 FLU+F+G4 4812.951 29 9683.903 9686.174 9819.611
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
208 FLU+F+I+G4 4803.431 30 9666.863 9669.294 9807.251
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
211 rtREV+G4 4791.371 10 9602.742 9603.022 9649.538
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
212 rtREV+I+G4 4785.918 11 9593.835 9594.172 9645.311
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
215 rtREV+F+G4 4761.112 29 9580.224 9582.496 9715.933
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
216 rtREV+F+I+G4 4755.824 30 9571.649 9574.080 9712.037
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
219 cpREV+G4 4757.639 10 9535.278 9535.558 9582.074
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
220 cpREV+I+G4 4754.045 11 9530.090 9530.427 9581.566
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
223 cpREV+F+G4 4741.076 29 9540.152 9542.424 9675.861
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
224 cpREV+F+I+G4 4737.617 30 9535.233 9537.665 9675.621
Akaike Information Criterion: cpREV+I+G4
Corrected Akaike Information Criterion: cpREV+I+G4
Bayesian Information Criterion: cpREV+I+G4
Best-fit model: cpREV+I+G4 chosen according to BIC
All model information printed to results/supermatrix/supermatrix.protein.fa.model.gz
CPU time for ModelFinder: 423.657 seconds (0h:7m:3s)
Wall-clock time for ModelFinder: 42.930 seconds (0h:0m:42s)
Generating 1000 samples for ultrafast bootstrap (seed: 413450)...
WARNING: 32 threads for alignment length 255 will slow down analysis
NOTE: 2 MB RAM (0 GB) is required!
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
Estimate model parameters (epsilon = 0.100)
Thoroughly optimizing +I+G parameters from 10 start values...
Init pinv, alpha: 0.000, 0.547 / Estimate: 0.000, 0.237 / LogL: -4757.639
Init pinv, alpha: 0.077, 0.547 / Estimate: 0.080, 0.273 / LogL: -4756.785
Init pinv, alpha: 0.154, 0.547 / Estimate: 0.158, 0.320 / LogL: -4755.996
Init pinv, alpha: 0.232, 0.547 / Estimate: 0.236, 0.381 / LogL: -4755.220
Init pinv, alpha: 0.309, 0.547 / Estimate: 0.314, 0.465 / LogL: -4754.490
Init pinv, alpha: 0.386, 0.547 / Estimate: 0.390, 0.589 / LogL: -4753.890
Init pinv, alpha: 0.463, 0.547 / Estimate: 0.463, 0.787 / LogL: -4753.615
Init pinv, alpha: 0.540, 0.547 / Estimate: 0.524, 1.098 / LogL: -4753.966
Init pinv, alpha: 0.618, 0.547 / Estimate: 0.531, 1.143 / LogL: -4754.058
Init pinv, alpha: 0.695, 0.547 / Estimate: 0.528, 1.124 / LogL: -4754.018
Optimal pinv,alpha: 0.463, 0.787 / LogL: -4753.615
Parameters optimization took 0.226 sec
Wrote distance file to...
Computing ML distances based on estimated model parameters...
Calculating distance matrix: done in 0.00506882 secs using 2506% CPU
Computing ML distances took 0.014985 sec (of wall-clock time) 0.403967 sec (of CPU time)
Setting up auxiliary I and S matrices: done in 0.0115662 secs using 10.94% CPU
Constructing RapidNJ tree: done in 0.0318284 secs using 125.4% CPU
Computing RapidNJ tree took 0.043559 sec (of wall-clock time) 0.043260 sec (of CPU time)
Log-likelihood of RapidNJ tree: -4753.615
--------------------------------------------------------------------
| INITIALIZING CANDIDATE TREE SET |
--------------------------------------------------------------------
Generating 99 parsimony trees... 0.031 second
Computing log-likelihood of 6 initial trees ... 1.552 seconds
Current best score: -4753.615
Do NNI search on 7 best initial trees
Optimizing NNI: done in 0.720347 secs using 1784% CPU
Optimizing NNI: done in 0.138424 secs using 2913% CPU
Optimizing NNI: done in 0.165715 secs using 3002% CPU
Optimizing NNI: done in 1.47911 secs using 1114% CPU
Optimizing NNI: done in 1.08875 secs using 1957% CPU
Optimizing NNI: done in 0.149227 secs using 2978% CPU
Optimizing NNI: done in 0.133209 secs using 3047% CPU
Finish initializing candidate tree set (7)
Current best tree score: -4753.615 / CPU time: 5.612
Number of iterations: 7
--------------------------------------------------------------------
| OPTIMIZING CANDIDATE TREE SET |
--------------------------------------------------------------------
Optimizing NNI: done in 0.135266 secs using 2953% CPU
Optimizing NNI: done in 0.0985645 secs using 3003% CPU
Optimizing NNI: done in 0.214924 secs using 2640% CPU
UPDATE BEST LOG-LIKELIHOOD: -4753.615
Iteration 10 / LogL: -4753.615 / Time: 0h:0m:6s
Optimizing NNI: done in 0.0817527 secs using 2936% CPU
Optimizing NNI: done in 0.755061 secs using 1910% CPU
Optimizing NNI: done in 1.48548 secs using 1452% CPU
Optimizing NNI: done in 1.27148 secs using 1544% CPU
Optimizing NNI: done in 0.771219 secs using 1744% CPU
Optimizing NNI: done in 0.0794607 secs using 2685% CPU
Optimizing NNI: done in 0.0861665 secs using 3023% CPU
Optimizing NNI: done in 0.123241 secs using 2993% CPU
Optimizing NNI: done in 0.0443138 secs using 2966% CPU
Optimizing NNI: done in 0.08832 secs using 3056% CPU
Iteration 20 / LogL: -4753.678 / Time: 0h:0m:11s
Optimizing NNI: done in 0.10284 secs using 3008% CPU
Optimizing NNI: done in 0.111081 secs using 3021% CPU
Optimizing NNI: done in 0.0957037 secs using 3059% CPU
Optimizing NNI: done in 0.126833 secs using 3030% CPU
Optimizing NNI: done in 0.0804382 secs using 2655% CPU
Optimizing NNI: done in 0.105339 secs using 3015% CPU
Optimizing NNI: done in 0.11242 secs using 3048% CPU
Optimizing NNI: done in 0.109235 secs using 3005% CPU
Optimizing NNI: done in 0.0791539 secs using 2993% CPU
UPDATE BEST LOG-LIKELIHOOD: -4753.615
Optimizing NNI: done in 0.0688421 secs using 3098% CPU
Iteration 30 / LogL: -4753.632 / Time: 0h:0m:12s (0h:0m:30s left)
Optimizing NNI: done in 0.0728304 secs using 3031% CPU
Optimizing NNI: done in 0.0832651 secs using 3028% CPU
Optimizing NNI: done in 0.0737372 secs using 3105% CPU
Optimizing NNI: done in 0.0763942 secs using 2982% CPU
Optimizing NNI: done in 0.108216 secs using 3027% CPU
Optimizing NNI: done in 0.110627 secs using 3020% CPU
Optimizing NNI: done in 0.0899857 secs using 2984% CPU
Optimizing NNI: done in 0.105611 secs using 2898% CPU
Optimizing NNI: done in 0.118817 secs using 3024% CPU
Optimizing NNI: done in 0.9553 secs using 945% CPU
Iteration 40 / LogL: -4761.550 / Time: 0h:0m:14s (0h:0m:22s left)
Optimizing NNI: done in 0.336211 secs using 2384% CPU
Optimizing NNI: done in 0.944855 secs using 1392% CPU
Optimizing NNI: done in 0.411638 secs using 1397% CPU
Optimizing NNI: done in 1.47347 secs using 1346% CPU
Optimizing NNI: done in 2.25614 secs using 1169% CPU
Optimizing NNI: done in 0.111458 secs using 3026% CPU
Optimizing NNI: done in 0.0819072 secs using 3004% CPU
Optimizing NNI: done in 1.30871 secs using 1288% CPU
Optimizing NNI: done in 2.59779 secs using 1165% CPU
Optimizing NNI: done in 0.0978691 secs using 3042% CPU
Iteration 50 / LogL: -4753.643 / Time: 0h:0m:24s (0h:0m:25s left)
Log-likelihood cutoff on original alignment: -4776.240
Optimizing NNI: done in 0.0846922 secs using 3068% CPU
Optimizing NNI: done in 4.27952 secs using 735.3% CPU
Optimizing NNI: done in 1.73447 secs using 786.6% CPU
Optimizing NNI: done in 0.341978 secs using 2777% CPU
Optimizing NNI: done in 0.0830223 secs using 3068% CPU
Optimizing NNI: done in 0.114455 secs using 2943% CPU
Optimizing NNI: done in 0.0535858 secs using 3050% CPU
Optimizing NNI: done in 0.152817 secs using 2473% CPU
Optimizing NNI: done in 1.14794 secs using 1303% CPU
Optimizing NNI: done in 0.354029 secs using 1637% CPU
Iteration 60 / LogL: -4759.702 / Time: 0h:0m:33s (0h:0m:22s left)
Optimizing NNI: done in 1.70927 secs using 1389% CPU
Optimizing NNI: done in 2.61792 secs using 1103% CPU
Optimizing NNI: done in 0.186399 secs using 2015% CPU
Optimizing NNI: done in 0.739696 secs using 1632% CPU
Optimizing NNI: done in 2.75194 secs using 967.1% CPU
Optimizing NNI: done in 0.78255 secs using 2496% CPU
Optimizing NNI: done in 0.131722 secs using 2975% CPU
UPDATE BEST LOG-LIKELIHOOD: -4753.615
Optimizing NNI: done in 0.108147 secs using 3003% CPU
Optimizing NNI: done in 0.081633 secs using 2962% CPU
UPDATE BEST LOG-LIKELIHOOD: -4753.615
Optimizing NNI: done in 5.14366 secs using 689.1% CPU
Iteration 70 / LogL: -4753.673 / Time: 0h:0m:47s (0h:0m:20s left)
Optimizing NNI: done in 1.83514 secs using 795.2% CPU
Optimizing NNI: done in 1.73305 secs using 825.1% CPU
Optimizing NNI: done in 2.49018 secs using 582% CPU
Optimizing NNI: done in 3.64304 secs using 824.8% CPU
Optimizing NNI: done in 3.00808 secs using 997.9% CPU
Optimizing NNI: done in 1.32291 secs using 2023% CPU
Optimizing NNI: done in 0.0874452 secs using 3072% CPU
Optimizing NNI: done in 0.092638 secs using 3050% CPU
Optimizing NNI: done in 0.0809316 secs using 3018% CPU
Optimizing NNI: done in 0.0858965 secs using 3042% CPU
Iteration 80 / LogL: -4753.617 / Time: 0h:1m:2s (0h:0m:15s left)
Optimizing NNI: done in 0.0696904 secs using 3009% CPU
Optimizing NNI: done in 0.135512 secs using 2950% CPU
Optimizing NNI: done in 0.0813792 secs using 3030% CPU
Optimizing NNI: done in 0.0807351 secs using 3011% CPU
Optimizing NNI: done in 0.0988241 secs using 3014% CPU
Optimizing NNI: done in 0.0910111 secs using 2972% CPU
Optimizing NNI: done in 0.0828082 secs using 2680% CPU
Optimizing NNI: done in 0.0791119 secs using 3042% CPU
Optimizing NNI: done in 0.0712579 secs using 3038% CPU
Optimizing NNI: done in 0.0714126 secs using 3081% CPU
Iteration 90 / LogL: -4753.624 / Time: 0h:1m:3s (0h:0m:7s left)
Optimizing NNI: done in 0.751056 secs using 1381% CPU
Optimizing NNI: done in 0.072715 secs using 3051% CPU
Optimizing NNI: done in 4.53437 secs using 769.2% CPU
Optimizing NNI: done in 1.33901 secs using 2243% CPU
Optimizing NNI: done in 0.410689 secs using 2853% CPU
Optimizing NNI: done in 0.0802805 secs using 3071% CPU
Optimizing NNI: done in 0.0876853 secs using 3048% CPU
Optimizing NNI: done in 0.0875309 secs using 2935% CPU
Optimizing NNI: done in 0.10691 secs using 2995% CPU
Optimizing NNI: done in 0.0810742 secs using 3040% CPU
Iteration 100 / LogL: -4753.615 / Time: 0h:1m:11s (0h:0m:0s left)
Log-likelihood cutoff on original alignment: -4776.240
NOTE: Bootstrap correlation coefficient of split occurrence frequencies: 1.000
Optimizing NNI: done in 0.169753 secs using 2607% CPU
TREE SEARCH COMPLETED AFTER 101 ITERATIONS / Time: 0h:1m:11s
--------------------------------------------------------------------
| FINALIZING TREE SEARCH |
--------------------------------------------------------------------
Performs final model parameters optimization
Estimate model parameters (epsilon = 0.010)
1. Initial log-likelihood: -4753.615
Optimal log-likelihood: -4753.615
Proportion of invariable sites: 0.463
Gamma shape alpha: 0.787
Parameters optimization took 1 rounds (0.256 sec)
BEST SCORE FOUND : -4753.615
Creating bootstrap support values...
Split supports printed to NEXUS file results/supermatrix/supermatrix.protein.fa.splits.nex
Total tree length: 0.870
Total number of iterations: 101
CPU time used for tree search: 942.632 sec (0h:15m:42s)
Wall-clock time used for tree search: 71.376 sec (0h:1m:11s)
Total CPU time used: 956.280 sec (0h:15m:56s)
Total wall-clock time used: 72.023 sec (0h:1m:12s)
Computing bootstrap consensus tree...
Reading input file results/supermatrix/supermatrix.protein.fa.splits.nex...
6 taxa and 13 splits.
Consensus tree written to results/supermatrix/supermatrix.protein.fa.contree
Reading input trees file results/supermatrix/supermatrix.protein.fa.contree
Log-likelihood of consensus tree: -4753.615
Analysis results written to:
IQ-TREE report: results/supermatrix/supermatrix.protein.fa.iqtree
Maximum-likelihood tree: results/supermatrix/supermatrix.protein.fa.treefile
Likelihood distances: results/supermatrix/supermatrix.protein.fa.mldist
Ultrafast bootstrap approximation results written to:
Split support values: results/supermatrix/supermatrix.protein.fa.splits.nex
Consensus tree: results/supermatrix/supermatrix.protein.fa.contree
Screen log file: results/supermatrix/supermatrix.protein.fa.log
ALISIM COMMAND
--------------
--alisim simulated_MSA -t results/supermatrix/supermatrix.protein.fa.treefile -m "cpREV+I{0.462593}+G4{0.787278}" --length 796
Date and Time: Sat Dec 16 03:42:26 2023
Gene Tree
OG0000004
IQ-TREE 2.2.0.3 COVID-edition built Aug 2 2022 Input file name: results/alignment/trimmed_protein/OG0000004.trimmed.protein.alignment.fa Type of analysis: ModelFinder + tree reconstruction + ultrafast bootstrap (1000 replicates) Random seed number: 257943 REFERENCES ---------- To cite IQ-TREE please use: Bui Quang Minh, Heiko A. Schmidt, Olga Chernomor, Dominik Schrempf, Michael D. Woodhams, Arndt von Haeseler, and Robert Lanfear (2020) IQ-TREE 2: New models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol., in press. https://doi.org/10.1093/molbev/msaa015 To cite ModelFinder please use: Subha Kalyaanamoorthy, Bui Quang Minh, Thomas KF Wong, Arndt von Haeseler, and Lars S Jermiin (2017) ModelFinder: Fast model selection for accurate phylogenetic estimates. Nature Methods, 14:587–589. https://doi.org/10.1038/nmeth.4285 Since you used ultrafast bootstrap (UFBoot) please also cite: Diep Thi Hoang, Olga Chernomor, Arndt von Haeseler, Bui Quang Minh, and Le Sy Vinh (2018) UFBoot2: Improving the ultrafast bootstrap approximation. Mol. Biol. Evol., 35:518–522. https://doi.org/10.1093/molbev/msx281 SEQUENCE ALIGNMENT ------------------ Input data: 6 sequences with 475 amino-acid sites Number of constant sites: 396 (= 83.3684% of all sites) Number of invariant (constant or ambiguous constant) sites: 396 (= 83.3684% of all sites) Number of parsimony informative sites: 41 Number of distinct site patterns: 96 ModelFinder ----------- Best-fit model according to BIC: WAG+I List of models sorted by BIC scores: Model LogL AIC w-AIC AICc w-AICc BIC w-BIC WAG+I -2120.007 4260.014 + 0.49 4260.488 + 0.491 4301.647 + 0.498 WAG+G4 -2120.007 4260.014 + 0.49 4260.488 + 0.491 4301.647 + 0.498 WAG+I+G4 -2122.300 4266.601 - 0.0182 4267.171 - 0.0174 4312.397 - 0.00231 JTT+I -2127.139 4274.278 - 0.000392 4274.752 - 0.000392 4315.911 - 0.000398 JTT+G4 -2127.151 4274.301 - 0.000387 4274.776 - 0.000388 4315.935 - 0.000394 Dayhoff+I -2133.718 4287.435 - 5.45e-07 4287.909 - 5.45e-07 4329.068 - 5.54e-07 Dayhoff+G4 -2133.723 4287.446 - 5.42e-07 4287.920 - 5.42e-07 4329.079 - 5.5e-07 cpREV+I -2134.481 4288.962 - 2.54e-07 4289.437 - 2.54e-07 4330.596 - 2.58e-07 cpREV+G4 -2134.497 4288.994 - 2.5e-07 4289.468 - 2.5e-07 4330.627 - 2.54e-07 VT+I -2135.653 4291.305 - 7.87e-08 4291.779 - 7.87e-08 4332.938 - 7.99e-08 VT+G4 -2135.655 4291.309 - 7.85e-08 4291.784 - 7.86e-08 4332.943 - 7.98e-08 Blosum62+I -2138.859 4297.718 - 3.19e-09 4298.192 - 3.19e-09 4339.351 - 3.24e-09 Blosum62+G4 -2138.860 4297.719 - 3.18e-09 4298.193 - 3.19e-09 4339.352 - 3.24e-09 rtREV+I -2144.872 4309.743 - 7.8e-12 4310.218 - 7.8e-12 4351.377 - 7.92e-12 rtREV+G4 -2144.874 4309.749 - 7.78e-12 4310.223 - 7.78e-12 4351.382 - 7.9e-12 WAG -2163.455 4344.910 - 1.8e-19 4345.297 - 1.88e-19 4382.380 - 1.47e-18 rtREV+F+I -2111.347 4280.694 - 1.58e-05 4284.604 - 2.85e-06 4401.430 - 1.07e-22 rtREV+F+G4 -2111.365 4280.730 - 1.56e-05 4284.640 - 2.79e-06 4401.466 - 1.05e-22 cpREV+F+I -2112.201 4282.402 - 6.75e-06 4286.312 - 1.21e-06 4403.138 - 4.56e-23 cpREV+F+G4 -2112.261 4282.522 - 6.35e-06 4286.432 - 1.14e-06 4403.258 - 4.3e-23 WAG+F+I -2114.683 4287.366 - 5.64e-07 4291.276 - 1.01e-07 4408.102 - 3.81e-24 WAG+F+G4 -2114.695 4287.389 - 5.57e-07 4291.299 - 1e-07 4408.125 - 3.77e-24 JTT+F+I -2119.341 4296.683 - 5.35e-09 4300.593 - 9.6e-10 4417.419 - 3.61e-26 JTT+F+G4 -2119.342 4296.684 - 5.34e-09 4300.594 - 9.6e-10 4417.420 - 3.61e-26 Dayhoff+F+I -2120.699 4299.397 - 1.38e-09 4303.307 - 2.47e-10 4420.133 - 9.3e-27 Dayhoff+F+G4 -2120.741 4299.482 - 1.32e-09 4303.392 - 2.37e-10 4420.219 - 8.92e-27 VT+F+I -2123.177 4304.354 - 1.15e-10 4308.264 - 2.07e-11 4425.090 - 7.8e-28 VT+F+G4 -2123.179 4304.358 - 1.15e-10 4308.268 - 2.07e-11 4425.094 - 7.79e-28 mtREV+F+I -2124.266 4306.532 - 3.88e-11 4310.442 - 6.98e-12 4427.268 - 2.63e-28 mtREV+F+G4 -2124.282 4306.565 - 3.82e-11 4310.475 - 6.86e-12 4427.301 - 2.58e-28 Blosum62+F+I -2130.497 4318.993 - 7.64e-14 4322.904 - 1.37e-14 4439.730 - 5.17e-31 Blosum62+F+G4 -2130.500 4319.000 - 7.62e-14 4322.911 - 1.37e-14 4439.737 - 5.15e-31 mtMAM+F+G4 -2142.106 4342.212 - 6.94e-19 4346.122 - 1.25e-19 4462.948 - 4.69e-36 mtMAM+F+I -2142.172 4342.344 - 6.5e-19 4346.254 - 1.17e-19 4463.080 - 4.39e-36 mtREV+I -2251.056 4522.112 - 5.98e-58 4522.586 - 5.98e-58 4563.745 - 6.08e-58 mtREV+G4 -2251.062 4522.124 - 5.95e-58 4522.598 - 5.95e-58 4563.757 - 6.04e-58 mtMAM+G4 -2271.402 4562.803 - 8.72e-67 4563.277 - 8.73e-67 4604.436 - 8.87e-67 mtMAM+I -2273.712 4567.424 - 8.66e-68 4567.898 - 8.66e-68 4609.057 - 8.8e-68 AIC, w-AIC : Akaike information criterion scores and weights. AICc, w-AICc : Corrected AIC scores and weights. BIC, w-BIC : Bayesian information criterion scores and weights. Plus signs denote the 95% confidence sets. Minus signs denote significant exclusion. SUBSTITUTION PROCESS -------------------- Model of substitution: WAG+I State frequencies: (model) Model of rate heterogeneity: Invar Proportion of invariable sites: 0.7495 Category Relative_rate Proportion 0 0 0.7495 1 3.9916 0.2505 MAXIMUM LIKELIHOOD TREE ----------------------- Log-likelihood of the tree: -2120.0067 (s.e. 71.5074) Unconstrained log-likelihood (without tree): -1691.3937 Number of free parameters (#branches + #model parameters): 10 Akaike information criterion (AIC) score: 4260.0135 Corrected Akaike information criterion (AICc) score: 4260.4876 Bayesian information criterion (BIC) score: 4301.6466 Total tree length (sum of branch lengths): 0.3046 Sum of internal branch lengths: 0.0921 (30.2353% of tree length) NOTE: Tree is UNROOTED although outgroup taxon 'Derbesia_sp_WEST4838' is drawn at root Numbers in parentheses are ultrafast bootstrap support (%) +---------------------------Derbesia_sp_WEST4838 | | +----------------Caulerpa_cliftonii_HV03798 | +--------------| (94) | | | +--------------Chlorodesmis_fastigiata_HV03865 | | +-------------| (90) | | +----Flabellia_petiolata_HV01202 +-------------| (89) | +--------------------Avrainvillea_mazei_HV02664 | +--------------Bryopsis_plumosa_WEST4718 Tree in newick format: (Derbesia_sp_WEST4838:0.0580456059,((Caulerpa_cliftonii_HV03798:0.0351341129,(Chlorodesmis_fastigiata_HV03865:0.0314733810,Flabellia_petiolata_HV01202:0.0114962939)90:0.0300035771)94:0.0327254398,Avrainvillea_mazei_HV02664:0.0442341248)89:0.0293797771,Bryopsis_plumosa_WEST4718:0.0321480445); CONSENSUS TREE -------------- Consensus tree is constructed from 1000 bootstrap trees Log-likelihood of consensus tree: -2120.006735 Robinson-Foulds distance between ML tree and consensus tree: 0 Branches with support >0.000000% are kept (extended consensus) Branch lengths are optimized by maximum likelihood on original alignment Numbers in parentheses are bootstrap supports (%) +---------------------------Derbesia_sp_WEST4838 | | +----------------Caulerpa_cliftonii_HV03798 | +--------------| (94) | | | +--------------Chlorodesmis_fastigiata_HV03865 | | +-------------| (90) | | +----Flabellia_petiolata_HV01202 +-------------| (89) | +--------------------Avrainvillea_mazei_HV02664 | +--------------Bryopsis_plumosa_WEST4718 Consensus tree in newick format: (Derbesia_sp_WEST4838:0.0580460245,((Caulerpa_cliftonii_HV03798:0.0351342873,(Chlorodesmis_fastigiata_HV03865:0.0314462823,Flabellia_petiolata_HV01202:0.0115038183)90:0.0299990221)94:0.0327241682,Avrainvillea_mazei_HV02664:0.0442315707)89:0.0293794353,Bryopsis_plumosa_WEST4718:0.0321461713); TIME STAMP ---------- Date and time: Sat Dec 16 03:40:23 2023 Total CPU time used: 3.800893 seconds (0h:0m:3s) Total wall-clock time used: 3.93933367 seconds (0h:0m:3s)
IQ-TREE multicore version 2.2.0.3 COVID-edition for Linux 64-bit built Aug 2 2022
Developed by Bui Quang Minh, James Barbetti, Nguyen Lam Tung,
Olga Chernomor, Heiko Schmidt, Dominik Schrempf, Michael Woodhams, Ly Trong Nhan.
Host: herbairumphylo (AVX512, FMA3, 283 GB RAM)
Command: iqtree2 -s results/alignment/trimmed_protein/OG0000004.trimmed.protein.alignment.fa -bb 1000 -m TEST -nt 1 -mset mrbayes -pre results/gene_tree/OG0000004/OG0000004.protein -redo
Seed: 257943 (Using SPRNG - Scalable Parallel Random Number Generator)
Time: Sat Dec 16 03:40:19 2023
Kernel: AVX+FMA - 1 threads (32 CPU cores detected)
HINT: Use -nt option to specify number of threads because your CPU has 32 cores!
HINT: -nt AUTO will automatically determine the best number of threads to use.
Reading alignment file results/alignment/trimmed_protein/OG0000004.trimmed.protein.alignment.fa ... Fasta format detected
Reading fasta file: done in 0.000103302 secs using 49.37% CPU
Alignment most likely contains protein sequences
Constructing alignment: done in 0.000379011 secs using 49.87% CPU
Alignment has 6 sequences with 475 columns, 96 distinct patterns
41 parsimony-informative, 38 singleton sites, 396 constant sites
Gap/Ambiguity Composition p-value
Analyzing sequences: done in 8.06153e-06 secs using 49.62% CPU
1 Derbesia_sp_WEST4838 0.00% passed 100.00%
2 Caulerpa_cliftonii_HV03798 0.00% passed 100.00%
3 Avrainvillea_mazei_HV02664 0.00% passed 100.00%
4 Chlorodesmis_fastigiata_HV03865 0.00% passed 100.00%
5 Flabellia_petiolata_HV01202 0.00% passed 100.00%
6 Bryopsis_plumosa_WEST4718 0.00% passed 100.00%
**** TOTAL 0.00% 0 sequences failed composition chi2 test (p-value<5%; df=19)
Checking for duplicate sequences: done in 1.77398e-05 secs using 45.1% CPU
Create initial parsimony tree by phylogenetic likelihood library (PLL)... 0.000 seconds
Perform fast likelihood tree search using LG+I+G model...
Estimate model parameters (epsilon = 5.000)
Perform nearest neighbor interchange...
Optimizing NNI: done in 0.00412478 secs using 99.06% CPU
Estimate model parameters (epsilon = 1.000)
1. Initial log-likelihood: -2120.899
Optimal log-likelihood: -2120.887
Proportion of invariable sites: 0.413
Gamma shape alpha: 0.341
Parameters optimization took 1 rounds (0.005 sec)
Time for fast ML tree search: 0.024 seconds
NOTE: ModelFinder requires 0 MB RAM!
ModelFinder will test up to 72 protein models (sample size: 475) ...
No. Model -LnL df AIC AICc BIC
1 WAG 2163.455 9 4344.910 4345.297 4382.380
2 WAG+I 2120.007 10 4260.014 4260.488 4301.647
3 WAG+G4 2120.007 10 4260.014 4260.488 4301.647
4 WAG+I+G4 2122.300 11 4266.601 4267.171 4312.397
6 WAG+F+I 2114.683 29 4287.366 4291.276 4408.102
7 WAG+F+G4 2114.695 29 4287.389 4291.299 4408.125
10 JTT+I 2127.139 10 4274.278 4274.752 4315.911
11 JTT+G4 2127.151 10 4274.301 4274.776 4315.935
14 JTT+F+I 2119.341 29 4296.683 4300.593 4417.419
15 JTT+F+G4 2119.342 29 4296.684 4300.594 4417.420
18 VT+I 2135.653 10 4291.305 4291.779 4332.938
19 VT+G4 2135.655 10 4291.309 4291.784 4332.943
22 VT+F+I 2123.177 29 4304.354 4308.264 4425.090
23 VT+F+G4 2123.179 29 4304.358 4308.268 4425.094
26 Blosum62+I 2138.859 10 4297.718 4298.192 4339.351
27 Blosum62+G4 2138.860 10 4297.719 4298.193 4339.352
30 Blosum62+F+I 2130.497 29 4318.993 4322.904 4439.730
31 Blosum62+F+G4 2130.500 29 4319.000 4322.911 4439.737
34 Dayhoff+I 2133.718 10 4287.435 4287.909 4329.068
35 Dayhoff+G4 2133.723 10 4287.446 4287.920 4329.079
38 Dayhoff+F+I 2120.699 29 4299.397 4303.307 4420.133
39 Dayhoff+F+G4 2120.741 29 4299.482 4303.392 4420.219
42 mtREV+I 2251.056 10 4522.112 4522.586 4563.745
43 mtREV+G4 2251.062 10 4522.124 4522.598 4563.757
46 mtREV+F+I 2124.266 29 4306.532 4310.442 4427.268
47 mtREV+F+G4 2124.282 29 4306.565 4310.475 4427.301
50 mtMAM+I 2273.712 10 4567.424 4567.898 4609.057
51 mtMAM+G4 2271.402 10 4562.803 4563.277 4604.436
54 mtMAM+F+I 2142.172 29 4342.344 4346.254 4463.080
55 mtMAM+F+G4 2142.106 29 4342.212 4346.122 4462.948
58 rtREV+I 2144.872 10 4309.743 4310.218 4351.377
59 rtREV+G4 2144.874 10 4309.749 4310.223 4351.382
62 rtREV+F+I 2111.347 29 4280.694 4284.604 4401.430
63 rtREV+F+G4 2111.365 29 4280.730 4284.640 4401.466
66 cpREV+I 2134.481 10 4288.962 4289.437 4330.596
67 cpREV+G4 2134.497 10 4288.994 4289.468 4330.627
70 cpREV+F+I 2112.201 29 4282.402 4286.312 4403.138
71 cpREV+F+G4 2112.261 29 4282.522 4286.432 4403.258
Akaike Information Criterion: WAG+I
Corrected Akaike Information Criterion: WAG+I
Bayesian Information Criterion: WAG+I
Best-fit model: WAG+I chosen according to BIC
All model information printed to results/gene_tree/OG0000004/OG0000004.protein.model.gz
CPU time for ModelFinder: 0.492 seconds (0h:0m:0s)
Wall-clock time for ModelFinder: 0.496 seconds (0h:0m:0s)
Generating 1000 samples for ultrafast bootstrap (seed: 257943)...
NOTE: 0 MB RAM (0 GB) is required!
Estimate model parameters (epsilon = 0.100)
1. Initial log-likelihood: -2120.007
Optimal log-likelihood: -2120.007
Proportion of invariable sites: 0.749
Parameters optimization took 1 rounds (0.001 sec)
Wrote distance file to...
Computing ML distances based on estimated model parameters...
Calculating distance matrix: done in 0.00134414 secs using 99.77% CPU
Computing ML distances took 0.001394 sec (of wall-clock time) 0.001397 sec (of CPU time)
Setting up auxiliary I and S matrices: done in 0.00109328 secs
Constructing RapidNJ tree: done in 4.63948e-05 secs
Computing RapidNJ tree took 0.001263 sec (of wall-clock time) 0.000077 sec (of CPU time)
Log-likelihood of RapidNJ tree: -2133.808
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| INITIALIZING CANDIDATE TREE SET |
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Generating 98 parsimony trees... 0.224 second
Computing log-likelihood of 5 initial trees ... 0.027 seconds
Current best score: -2120.007
Do NNI search on 7 best initial trees
Optimizing NNI: done in 0.0225022 secs using 99.67% CPU
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Optimizing NNI: done in 0.0406144 secs using 99.87% CPU
Finish initializing candidate tree set (7)
Current best tree score: -2120.007 / CPU time: 0.567
Number of iterations: 7
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| OPTIMIZING CANDIDATE TREE SET |
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Optimizing NNI: done in 0.0377623 secs using 99.86% CPU
Optimizing NNI: done in 0.0643155 secs using 93.73% CPU
Optimizing NNI: done in 0.0371835 secs using 99.81% CPU
UPDATE BEST LOG-LIKELIHOOD: -2120.007
Iteration 10 / LogL: -2120.007 / Time: 0h:0m:0s
Optimizing NNI: done in 0.015947 secs using 99.86% CPU
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Optimizing NNI: done in 0.0606889 secs using 93.53% CPU
Optimizing NNI: done in 0.036185 secs using 88.94% CPU
UPDATE BEST LOG-LIKELIHOOD: -2120.007
Optimizing NNI: done in 0.0404688 secs using 89.92% CPU
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Iteration 20 / LogL: -2120.012 / Time: 0h:0m:1s
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Iteration 30 / LogL: -2120.010 / Time: 0h:0m:1s (0h:0m:4s left)
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Iteration 40 / LogL: -2120.019 / Time: 0h:0m:2s (0h:0m:3s left)
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Optimizing NNI: done in 0.0485788 secs using 75.32% CPU
Iteration 50 / LogL: -2120.009 / Time: 0h:0m:2s (0h:0m:3s left)
Log-likelihood cutoff on original alignment: -2153.940
Optimizing NNI: done in 0.0204678 secs using 99.87% CPU
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Iteration 60 / LogL: -2120.012 / Time: 0h:0m:3s (0h:0m:2s left)
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Iteration 70 / LogL: -2125.044 / Time: 0h:0m:3s (0h:0m:1s left)
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Iteration 80 / LogL: -2120.009 / Time: 0h:0m:3s (0h:0m:0s left)
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Iteration 90 / LogL: -2120.010 / Time: 0h:0m:3s (0h:0m:0s left)
Optimizing NNI: done in 0.00367647 secs using 99.93% CPU
Optimizing NNI: done in 0.00369476 secs using 99.95% CPU
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Optimizing NNI: done in 0.0037899 secs using 99.95% CPU
Optimizing NNI: done in 0.0037866 secs using 99.96% CPU
Optimizing NNI: done in 0.00373302 secs using 99.97% CPU
Iteration 100 / LogL: -2120.014 / Time: 0h:0m:3s (0h:0m:0s left)
Log-likelihood cutoff on original alignment: -2153.393
NOTE: Bootstrap correlation coefficient of split occurrence frequencies: 1.000
Optimizing NNI: done in 0.00383049 secs using 99.93% CPU
TREE SEARCH COMPLETED AFTER 101 ITERATIONS / Time: 0h:0m:3s
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| FINALIZING TREE SEARCH |
--------------------------------------------------------------------
Performs final model parameters optimization
Estimate model parameters (epsilon = 0.010)
1. Initial log-likelihood: -2120.007
Optimal log-likelihood: -2120.007
Proportion of invariable sites: 0.749
Parameters optimization took 1 rounds (0.001 sec)
BEST SCORE FOUND : -2120.007
Creating bootstrap support values...
Split supports printed to NEXUS file results/gene_tree/OG0000004/OG0000004.protein.splits.nex
Total tree length: 0.305
Total number of iterations: 101
CPU time used for tree search: 3.746 sec (0h:0m:3s)
Wall-clock time used for tree search: 3.867 sec (0h:0m:3s)
Total CPU time used: 3.801 sec (0h:0m:3s)
Total wall-clock time used: 3.931 sec (0h:0m:3s)
Computing bootstrap consensus tree...
Reading input file results/gene_tree/OG0000004/OG0000004.protein.splits.nex...
6 taxa and 17 splits.
Consensus tree written to results/gene_tree/OG0000004/OG0000004.protein.contree
Reading input trees file results/gene_tree/OG0000004/OG0000004.protein.contree
Log-likelihood of consensus tree: -2120.007
Analysis results written to:
IQ-TREE report: results/gene_tree/OG0000004/OG0000004.protein.iqtree
Maximum-likelihood tree: results/gene_tree/OG0000004/OG0000004.protein.treefile
Likelihood distances: results/gene_tree/OG0000004/OG0000004.protein.mldist
Ultrafast bootstrap approximation results written to:
Split support values: results/gene_tree/OG0000004/OG0000004.protein.splits.nex
Consensus tree: results/gene_tree/OG0000004/OG0000004.protein.contree
Screen log file: results/gene_tree/OG0000004/OG0000004.protein.log
ALISIM COMMAND
--------------
--alisim simulated_MSA -t results/gene_tree/OG0000004/OG0000004.protein.treefile -m "WAG+I{0.749472}" --length 475
Date and Time: Sat Dec 16 03:40:23 2023
OG0000000
IQ-TREE 2.2.0.3 COVID-edition built Aug 2 2022 Input file name: results/alignment/trimmed_protein/OG0000000.trimmed.protein.alignment.fa Type of analysis: ModelFinder + tree reconstruction + ultrafast bootstrap (1000 replicates) Random seed number: 352098 REFERENCES ---------- To cite IQ-TREE please use: Bui Quang Minh, Heiko A. Schmidt, Olga Chernomor, Dominik Schrempf, Michael D. Woodhams, Arndt von Haeseler, and Robert Lanfear (2020) IQ-TREE 2: New models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol., in press. https://doi.org/10.1093/molbev/msaa015 To cite ModelFinder please use: Subha Kalyaanamoorthy, Bui Quang Minh, Thomas KF Wong, Arndt von Haeseler, and Lars S Jermiin (2017) ModelFinder: Fast model selection for accurate phylogenetic estimates. Nature Methods, 14:587–589. https://doi.org/10.1038/nmeth.4285 Since you used ultrafast bootstrap (UFBoot) please also cite: Diep Thi Hoang, Olga Chernomor, Arndt von Haeseler, Bui Quang Minh, and Le Sy Vinh (2018) UFBoot2: Improving the ultrafast bootstrap approximation. Mol. Biol. Evol., 35:518–522. https://doi.org/10.1093/molbev/msx281 SEQUENCE ALIGNMENT ------------------ Input data: 6 sequences with 321 amino-acid sites Number of constant sites: 157 (= 48.9097% of all sites) Number of invariant (constant or ambiguous constant) sites: 157 (= 48.9097% of all sites) Number of parsimony informative sites: 59 Number of distinct site patterns: 180 ModelFinder ----------- Best-fit model according to BIC: cpREV+F+G4 List of models sorted by BIC scores: Model LogL AIC w-AIC AICc w-AICc BIC w-BIC cpREV+F+G4 -2488.430 5034.859 + 0.707 5040.839 + 0.748 5144.231 + 0.922 cpREV+F+I+G4 -2488.377 5036.753 + 0.274 5043.167 + 0.233 5149.896 + 0.0543 WAG+F+G4 -2492.506 5043.012 - 0.012 5048.991 - 0.0127 5152.384 - 0.0156 cpREV+G4 -2548.500 5116.999 - 1.03e-18 5117.709 - 1.52e-17 5154.714 - 0.00488 rtREV+F+G4 -2494.904 5047.809 - 0.00109 5053.788 - 0.00115 5157.181 - 0.00142 WAG+F+I+G4 -2492.396 5044.793 - 0.00492 5051.207 - 0.00419 5157.936 - 0.000975 cpREV+I+G4 -2548.319 5118.637 - 4.54e-19 5119.491 - 6.23e-18 5160.123 - 0.000327 VT+F+G4 -2497.491 5052.982 - 8.21e-05 5058.962 - 8.68e-05 5162.354 - 0.000107 rtREV+F+I+G4 -2494.939 5049.878 - 0.000387 5056.292 - 0.00033 5163.022 - 7.66e-05 Dayhoff+F+G4 -2497.932 5053.864 - 5.28e-05 5059.843 - 5.59e-05 5163.235 - 6.89e-05 JTT+F+G4 -2497.977 5053.954 - 5.05e-05 5059.933 - 5.34e-05 5163.325 - 6.58e-05 mtREV+F+G4 -2499.045 5056.089 - 1.74e-05 5062.069 - 1.84e-05 5165.461 - 2.26e-05 VT+F+I+G4 -2497.490 5054.981 - 3.02e-05 5061.395 - 2.57e-05 5168.124 - 5.98e-06 JTT+F+I+G4 -2498.056 5056.112 - 1.72e-05 5062.525 - 1.46e-05 5169.255 - 3.4e-06 Dayhoff+F+I+G4 -2498.105 5056.209 - 1.63e-05 5062.623 - 1.39e-05 5169.353 - 3.23e-06 mtREV+F+I+G4 -2499.164 5058.328 - 5.67e-06 5064.742 - 4.82e-06 5171.472 - 1.12e-06 Blosum62+F+G4 -2506.600 5071.200 - 9.08e-09 5077.179 - 9.61e-09 5180.572 - 1.18e-08 rtREV+G4 -2562.607 5145.214 - 7.69e-25 5145.924 - 1.13e-23 5182.929 - 3.65e-09 Blosum62+F+I+G4 -2506.527 5073.054 - 3.59e-09 5079.467 - 3.06e-09 5186.197 - 7.11e-10 rtREV+I+G4 -2562.562 5147.124 - 2.96e-25 5147.978 - 4.06e-24 5188.609 - 2.13e-10 mtMAM+F+G4 -2512.785 5083.569 - 1.87e-11 5089.549 - 1.98e-11 5192.941 - 2.44e-11 VT+G4 -2568.199 5156.398 - 2.87e-27 5157.107 - 4.23e-26 5194.112 - 1.36e-11 VT+I+G4 -2568.001 5158.002 - 1.29e-27 5158.857 - 1.76e-26 5199.488 - 9.24e-13 mtMAM+F+I+G4 -2513.259 5086.518 - 4.29e-12 5092.931 - 3.65e-12 5199.661 - 8.48e-13 WAG+G4 -2582.830 5185.660 - 1.27e-33 5186.370 - 1.87e-32 5223.375 - 6.01e-18 Blosum62+G4 -2583.604 5187.208 - 5.85e-34 5187.917 - 8.63e-33 5224.922 - 2.77e-18 JTT+G4 -2583.751 5187.502 - 5.05e-34 5188.211 - 7.45e-33 5225.216 - 2.39e-18 WAG+I+G4 -2582.425 5186.849 - 7e-34 5187.704 - 9.6e-33 5228.335 - 5.03e-19 Blosum62+I+G4 -2583.371 5188.741 - 2.72e-34 5189.595 - 3.73e-33 5230.227 - 1.95e-19 JTT+I+G4 -2583.689 5189.378 - 1.98e-34 5190.232 - 2.71e-33 5230.864 - 1.42e-19 WAG+I -2600.817 5221.633 - 1.96e-41 5222.343 - 2.89e-40 5259.347 - 9.28e-26 Dayhoff+G4 -2601.194 5222.388 - 1.34e-41 5223.098 - 1.98e-40 5260.103 - 6.36e-26 Dayhoff+I+G4 -2600.845 5223.689 - 7.01e-42 5224.544 - 9.61e-41 5265.175 - 5.04e-27 mtREV+G4 -2642.699 5305.399 - 1.27e-59 5306.108 - 1.87e-58 5343.113 - 6e-44 mtREV+I+G4 -2643.751 5309.501 - 1.63e-60 5310.356 - 2.23e-59 5350.987 - 1.17e-45 mtMAM+G4 -2662.538 5345.076 - 3.07e-68 5345.785 - 4.52e-67 5382.790 - 1.45e-52 mtMAM+I+G4 -2663.828 5349.655 - 3.11e-69 5350.509 - 4.26e-68 5391.141 - 2.23e-54 WAG -2675.677 5369.354 - 1.64e-73 5369.933 - 2.58e-72 5403.297 - 5.12e-57 AIC, w-AIC : Akaike information criterion scores and weights. AICc, w-AICc : Corrected AIC scores and weights. BIC, w-BIC : Bayesian information criterion scores and weights. Plus signs denote the 95% confidence sets. Minus signs denote significant exclusion. SUBSTITUTION PROCESS -------------------- Model of substitution: cpREV+F+G4 State frequencies: (empirical counts from alignment) pi(A) = 0.0458 pi(R) = 0.0261 pi(N) = 0.0735 pi(D) = 0.0293 pi(C) = 0.0080 pi(Q) = 0.0831 pi(E) = 0.0479 pi(G) = 0.0666 pi(H) = 0.0032 pi(I) = 0.0974 pi(L) = 0.0900 pi(K) = 0.0831 pi(M) = 0.0096 pi(F) = 0.0575 pi(P) = 0.0767 pi(S) = 0.0490 pi(T) = 0.0357 pi(W) = 0.0005 pi(Y) = 0.0362 pi(V) = 0.0809 Model of rate heterogeneity: Gamma with 4 categories Gamma shape alpha: 0.3979 Category Relative_rate Proportion 1 0.0164 0.2500 2 0.1802 0.2500 3 0.7290 0.2500 4 3.0743 0.2500 Relative rates are computed as MEAN of the portion of the Gamma distribution falling in the category. MAXIMUM LIKELIHOOD TREE ----------------------- Log-likelihood of the tree: -2488.4295 (s.e. 90.5758) Unconstrained log-likelihood (without tree): -1484.2566 Number of free parameters (#branches + #model parameters): 29 Akaike information criterion (AIC) score: 5034.8590 Corrected Akaike information criterion (AICc) score: 5040.8383 Bayesian information criterion (BIC) score: 5144.2307 Total tree length (sum of branch lengths): 2.1759 Sum of internal branch lengths: 0.4676 (21.4925% of tree length) NOTE: Tree is UNROOTED although outgroup taxon 'Derbesia_sp_WEST4838' is drawn at root Numbers in parentheses are ultrafast bootstrap support (%) +---------------------------Derbesia_sp_WEST4838 | | +--------------------------Caulerpa_cliftonii_HV03798 | +------------------| (99) | | | +--------------Chlorodesmis_fastigiata_HV03865 | | +--| (70) | | +--Flabellia_petiolata_HV01202 +-----------| (92) | +---------------------------------------Avrainvillea_mazei_HV02664 | +--------------Bryopsis_plumosa_WEST4718 Tree in newick format: (Derbesia_sp_WEST4838:0.3677072936,((Caulerpa_cliftonii_HV03798:0.3634465577,(Chlorodesmis_fastigiata_HV03865:0.1962104704,Flabellia_petiolata_HV01202:0.0517974849)70:0.0511600796)99:0.2520130494,Avrainvillea_mazei_HV02664:0.5244432155)92:0.1644736496,Bryopsis_plumosa_WEST4718:0.2046050269); CONSENSUS TREE -------------- Consensus tree is constructed from 1000 bootstrap trees Log-likelihood of consensus tree: -2488.429484 Robinson-Foulds distance between ML tree and consensus tree: 0 Branches with support >0.000000% are kept (extended consensus) Branch lengths are optimized by maximum likelihood on original alignment Numbers in parentheses are bootstrap supports (%) +---------------------------Derbesia_sp_WEST4838 | | +--------------------------Caulerpa_cliftonii_HV03798 | +------------------| (99) | | | +--------------Chlorodesmis_fastigiata_HV03865 | | +--| (70) | | +--Flabellia_petiolata_HV01202 +-----------| (92) | +---------------------------------------Avrainvillea_mazei_HV02664 | +--------------Bryopsis_plumosa_WEST4718 Consensus tree in newick format: (Derbesia_sp_WEST4838:0.3677153709,((Caulerpa_cliftonii_HV03798:0.3636555020,(Chlorodesmis_fastigiata_HV03865:0.1963020222,Flabellia_petiolata_HV01202:0.0517839535)70:0.0510345895)99:0.2520924593,Avrainvillea_mazei_HV02664:0.5245226460)92:0.1645047599,Bryopsis_plumosa_WEST4718:0.2046481196); TIME STAMP ---------- Date and time: Sat Dec 16 03:40:24 2023 Total CPU time used: 3.826684 seconds (0h:0m:3s) Total wall-clock time used: 3.902106747 seconds (0h:0m:3s)
IQ-TREE multicore version 2.2.0.3 COVID-edition for Linux 64-bit built Aug 2 2022
Developed by Bui Quang Minh, James Barbetti, Nguyen Lam Tung,
Olga Chernomor, Heiko Schmidt, Dominik Schrempf, Michael Woodhams, Ly Trong Nhan.
Host: herbairumphylo (AVX512, FMA3, 283 GB RAM)
Command: iqtree2 -s results/alignment/trimmed_protein/OG0000000.trimmed.protein.alignment.fa -bb 1000 -m TEST -nt 1 -mset mrbayes -pre results/gene_tree/OG0000000/OG0000000.protein -redo
Seed: 352098 (Using SPRNG - Scalable Parallel Random Number Generator)
Time: Sat Dec 16 03:40:19 2023
Kernel: AVX+FMA - 1 threads (32 CPU cores detected)
HINT: Use -nt option to specify number of threads because your CPU has 32 cores!
HINT: -nt AUTO will automatically determine the best number of threads to use.
Reading alignment file results/alignment/trimmed_protein/OG0000000.trimmed.protein.alignment.fa ... Fasta format detected
Reading fasta file: done in 8.47355e-05 secs using 97.95% CPU
Alignment most likely contains protein sequences
Constructing alignment: done in 0.000369027 secs using 99.72% CPU
Alignment has 6 sequences with 321 columns, 180 distinct patterns
59 parsimony-informative, 105 singleton sites, 157 constant sites
Gap/Ambiguity Composition p-value
Analyzing sequences: done in 2.12863e-05 secs
1 Derbesia_sp_WEST4838 1.56% passed 98.56%
2 Caulerpa_cliftonii_HV03798 0.00% passed 99.95%
3 Avrainvillea_mazei_HV02664 2.80% passed 97.88%
4 Chlorodesmis_fastigiata_HV03865 3.74% passed 99.98%
5 Flabellia_petiolata_HV01202 5.92% passed 100.00%
6 Bryopsis_plumosa_WEST4718 0.93% passed 99.99%
**** TOTAL 2.49% 0 sequences failed composition chi2 test (p-value<5%; df=19)
Checking for duplicate sequences: done in 2.28807e-05 secs
Create initial parsimony tree by phylogenetic likelihood library (PLL)... 0.000 seconds
Perform fast likelihood tree search using LG+I+G model...
Estimate model parameters (epsilon = 5.000)
Perform nearest neighbor interchange...
Optimizing NNI: done in 0.0209788 secs using 99.93% CPU
Estimate model parameters (epsilon = 1.000)
1. Initial log-likelihood: -2562.958
Optimal log-likelihood: -2562.892
Proportion of invariable sites: 0.222
Gamma shape alpha: 0.590
Parameters optimization took 1 rounds (0.011 sec)
Time for fast ML tree search: 0.071 seconds
NOTE: ModelFinder requires 1 MB RAM!
ModelFinder will test up to 72 protein models (sample size: 321) ...
No. Model -LnL df AIC AICc BIC
1 WAG 2675.677 9 5369.354 5369.933 5403.297
2 WAG+I 2600.817 10 5221.633 5222.343 5259.347
3 WAG+G4 2582.830 10 5185.660 5186.370 5223.375
4 WAG+I+G4 2582.425 11 5186.849 5187.704 5228.335
7 WAG+F+G4 2492.506 29 5043.012 5048.991 5152.384
8 WAG+F+I+G4 2492.396 30 5044.793 5051.207 5157.936
11 JTT+G4 2583.751 10 5187.502 5188.211 5225.216
12 JTT+I+G4 2583.689 11 5189.378 5190.232 5230.864
15 JTT+F+G4 2497.977 29 5053.954 5059.933 5163.325
16 JTT+F+I+G4 2498.056 30 5056.112 5062.525 5169.255
19 VT+G4 2568.199 10 5156.398 5157.107 5194.112
20 VT+I+G4 2568.001 11 5158.002 5158.857 5199.488
23 VT+F+G4 2497.491 29 5052.982 5058.962 5162.354
24 VT+F+I+G4 2497.490 30 5054.981 5061.395 5168.124
27 Blosum62+G4 2583.604 10 5187.208 5187.917 5224.922
28 Blosum62+I+G4 2583.371 11 5188.741 5189.595 5230.227
31 Blosum62+F+G4 2506.600 29 5071.200 5077.179 5180.572
32 Blosum62+F+I+G4 2506.527 30 5073.054 5079.467 5186.197
35 Dayhoff+G4 2601.194 10 5222.388 5223.098 5260.103
36 Dayhoff+I+G4 2600.845 11 5223.689 5224.544 5265.175
39 Dayhoff+F+G4 2497.932 29 5053.864 5059.843 5163.235
40 Dayhoff+F+I+G4 2498.105 30 5056.209 5062.623 5169.353
43 mtREV+G4 2642.699 10 5305.399 5306.108 5343.113
44 mtREV+I+G4 2643.751 11 5309.501 5310.356 5350.987
47 mtREV+F+G4 2499.045 29 5056.089 5062.069 5165.461
48 mtREV+F+I+G4 2499.164 30 5058.328 5064.742 5171.472
51 mtMAM+G4 2662.538 10 5345.076 5345.785 5382.790
52 mtMAM+I+G4 2663.828 11 5349.655 5350.509 5391.141
55 mtMAM+F+G4 2512.785 29 5083.569 5089.549 5192.941
56 mtMAM+F+I+G4 2513.259 30 5086.518 5092.931 5199.661
59 rtREV+G4 2562.607 10 5145.214 5145.924 5182.929
60 rtREV+I+G4 2562.562 11 5147.124 5147.978 5188.609
63 rtREV+F+G4 2494.904 29 5047.809 5053.788 5157.181
64 rtREV+F+I+G4 2494.939 30 5049.878 5056.292 5163.022
67 cpREV+G4 2548.500 10 5116.999 5117.709 5154.714
68 cpREV+I+G4 2548.319 11 5118.637 5119.491 5160.123
71 cpREV+F+G4 2488.430 29 5034.859 5040.839 5144.231
72 cpREV+F+I+G4 2488.377 30 5036.753 5043.167 5149.896
Akaike Information Criterion: cpREV+F+G4
Corrected Akaike Information Criterion: cpREV+F+G4
Bayesian Information Criterion: cpREV+F+G4
Best-fit model: cpREV+F+G4 chosen according to BIC
All model information printed to results/gene_tree/OG0000000/OG0000000.protein.model.gz
CPU time for ModelFinder: 1.238 seconds (0h:0m:1s)
Wall-clock time for ModelFinder: 1.276 seconds (0h:0m:1s)
Generating 1000 samples for ultrafast bootstrap (seed: 352098)...
NOTE: 1 MB RAM (0 GB) is required!
Estimate model parameters (epsilon = 0.100)
1. Initial log-likelihood: -2488.430
Optimal log-likelihood: -2488.429
Gamma shape alpha: 0.398
Parameters optimization took 1 rounds (0.009 sec)
Wrote distance file to...
Computing ML distances based on estimated model parameters...
Calculating distance matrix: done in 0.00803835 secs using 99.97% CPU
Computing ML distances took 0.008159 sec (of wall-clock time) 0.008164 sec (of CPU time)
Setting up auxiliary I and S matrices: done in 6.36801e-05 secs
Constructing RapidNJ tree: done in 4.51952e-05 secs
Computing RapidNJ tree took 0.000303 sec (of wall-clock time) 0.000000 sec (of CPU time)
Log-likelihood of RapidNJ tree: -2488.429
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| INITIALIZING CANDIDATE TREE SET |
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Generating 99 parsimony trees... 0.028 second
Computing log-likelihood of 6 initial trees ... 0.018 seconds
Current best score: -2488.429
Do NNI search on 7 best initial trees
Optimizing NNI: done in 0.0147182 secs using 99.92% CPU
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Finish initializing candidate tree set (7)
Current best tree score: -2488.429 / CPU time: 0.298
Number of iterations: 7
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| OPTIMIZING CANDIDATE TREE SET |
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Optimizing NNI: done in 0.0294365 secs using 99.95% CPU
UPDATE BEST LOG-LIKELIHOOD: -2488.429
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Iteration 10 / LogL: -2488.434 / Time: 0h:0m:0s
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UPDATE BEST LOG-LIKELIHOOD: -2488.429
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Iteration 20 / LogL: -2492.515 / Time: 0h:0m:0s
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UPDATE BEST LOG-LIKELIHOOD: -2488.429
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Iteration 30 / LogL: -2488.487 / Time: 0h:0m:1s (0h:0m:2s left)
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Iteration 40 / LogL: -2488.429 / Time: 0h:0m:1s (0h:0m:2s left)
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Iteration 50 / LogL: -2488.553 / Time: 0h:0m:1s (0h:0m:1s left)
Log-likelihood cutoff on original alignment: -2508.051
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Iteration 60 / LogL: -2488.495 / Time: 0h:0m:2s (0h:0m:1s left)
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Iteration 70 / LogL: -2488.429 / Time: 0h:0m:2s (0h:0m:1s left)
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Iteration 80 / LogL: -2488.563 / Time: 0h:0m:2s (0h:0m:0s left)
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Iteration 90 / LogL: -2488.553 / Time: 0h:0m:3s (0h:0m:0s left)
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Optimizing NNI: done in 0.0528668 secs using 99.99% CPU
Iteration 100 / LogL: -2488.629 / Time: 0h:0m:3s (0h:0m:0s left)
Log-likelihood cutoff on original alignment: -2507.874
NOTE: Bootstrap correlation coefficient of split occurrence frequencies: 1.000
Optimizing NNI: done in 0.0340691 secs using 99.98% CPU
TREE SEARCH COMPLETED AFTER 101 ITERATIONS / Time: 0h:0m:3s
--------------------------------------------------------------------
| FINALIZING TREE SEARCH |
--------------------------------------------------------------------
Performs final model parameters optimization
Estimate model parameters (epsilon = 0.010)
1. Initial log-likelihood: -2488.429
Optimal log-likelihood: -2488.429
Gamma shape alpha: 0.398
Parameters optimization took 1 rounds (0.013 sec)
BEST SCORE FOUND : -2488.429
Creating bootstrap support values...
Split supports printed to NEXUS file results/gene_tree/OG0000000/OG0000000.protein.splits.nex
Total tree length: 2.176
Total number of iterations: 101
CPU time used for tree search: 3.740 sec (0h:0m:3s)
Wall-clock time used for tree search: 3.776 sec (0h:0m:3s)
Total CPU time used: 3.827 sec (0h:0m:3s)
Total wall-clock time used: 3.869 sec (0h:0m:3s)
Computing bootstrap consensus tree...
Reading input file results/gene_tree/OG0000000/OG0000000.protein.splits.nex...
6 taxa and 15 splits.
Consensus tree written to results/gene_tree/OG0000000/OG0000000.protein.contree
Reading input trees file results/gene_tree/OG0000000/OG0000000.protein.contree
Log-likelihood of consensus tree: -2488.429
Analysis results written to:
IQ-TREE report: results/gene_tree/OG0000000/OG0000000.protein.iqtree
Maximum-likelihood tree: results/gene_tree/OG0000000/OG0000000.protein.treefile
Likelihood distances: results/gene_tree/OG0000000/OG0000000.protein.mldist
Ultrafast bootstrap approximation results written to:
Split support values: results/gene_tree/OG0000000/OG0000000.protein.splits.nex
Consensus tree: results/gene_tree/OG0000000/OG0000000.protein.contree
Screen log file: results/gene_tree/OG0000000/OG0000000.protein.log
ALISIM COMMAND
--------------
--alisim simulated_MSA -t results/gene_tree/OG0000000/OG0000000.protein.treefile -m "cpREV+F+G4{0.397939}" --length 321
Date and Time: Sat Dec 16 03:40:24 2023
Supertree
Workflow
Bibliography
- 1
- Deren A. R. Eaton. Toytree: A minimalist tree visualization and manipulation library for Python. Methods in Ecology and Evolution, 11:187–191, 2020. doi:10.1111/2041-210X.13313.
- 2
- David M. Emms and Steven Kelly. Orthofinder: phylogenetic orthology inference for comparative genomics. Genome Biology, 20(1):238, 2019. URL: https://doi.org/10.1186/s13059-019-1832-y, doi:10.1186/s13059-019-1832-y.
- 3
- Diep Thi Hoang, Olga Chernomor, Arndt von Haeseler, Bui Quang Minh, and Le Sy Vinh. UFBoot2: Improving the Ultrafast Bootstrap Approximation. Molecular Biology and Evolution, 35(2):518–522, 10 2017. URL: https://doi.org/10.1093/molbev/msx281, arXiv:https://academic.oup.com/mbe/article-pdf/35/2/518/24367824/msx281.pdf, doi:10.1093/molbev/msx281.
- 4
- Subha Kalyaanamoorthy, Bui Quang Minh, Thomas K F Wong, Arndt von Haeseler, and Lars S Jermiin. Modelfinder: fast model selection for accurate phylogenetic estimates. Nature Methods, 14(6):587–589, 2017. URL: https://doi.org/10.1038/nmeth.4285, doi:10.1038/nmeth.4285.
- 5
- Kazutaka Katoh and Daron M. Standley. MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability. Molecular Biology and Evolution, 30(4):772–780, 01 2013. URL: https://doi.org/10.1093/molbev/mst010, arXiv:https://academic.oup.com/mbe/article-pdf/30/4/772/6420419/mst010.pdf, doi:10.1093/molbev/mst010.
- 6
- Bui Quang Minh, Heiko A Schmidt, Olga Chernomor, Dominik Schrempf, Michael D Woodhams, Arndt von Haeseler, and Robert Lanfear. IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era. Molecular Biology and Evolution, 37(5):1530–1534, 02 2020. URL: https://doi.org/10.1093/molbev/msaa015, arXiv:https://academic.oup.com/mbe/article-pdf/37/5/1530/33386032/msaa015.pdf, doi:10.1093/molbev/msaa015.
- 7
- Jacob L Steenwyk, III Buida, Thomas J, Abigail L Labella, Yuanning Li, Xing-Xing Shen, and Antonis Rokas. PhyKIT: a broadly applicable UNIX shell toolkit for processing and analyzing phylogenomic data. Bioinformatics, 37(16):2325–2331, 02 2021. URL: https://doi.org/10.1093/bioinformatics/btab096, arXiv:https://academic.oup.com/bioinformatics/article-pdf/37/16/2325/39948152/btab096.pdf, doi:10.1093/bioinformatics/btab096.
- 8
- Jacob L. Steenwyk, Thomas J. Buida, Carla Gonçalves, Dayna C. Goltz, Grace Morales, Matthew E. Mead, Abigail L. LaBella, Christina M. Chavez, Jonathan E. Schmitz, Maria Hadjifrangiskou, Yuanning Li, and Antonis Rokas. BioKIT: a versatile toolkit for processing and analyzing diverse types of sequence data. biorxiv, oct 2021. URL: https://doi.org/10.1101%2F2021.10.02.462868, doi:10.1101/2021.10.02.462868.
- 9
- Jacob L. Steenwyk, Thomas J. Buida, III, Yuanning Li, Xing-Xing Shen, and Antonis Rokas. Clipkit: a multiple sequence alignment trimming software for accurate phylogenomic inference. PLOS Biology, 18(12):1–17, 12 2020. URL: https://doi.org/10.1371/journal.pbio.3001007, doi:10.1371/journal.pbio.3001007.
- 10
- Chao Zhang, Maryam Rabiee, Erfan Sayyari, and Siavash Mirarab. Astral-iii: polynomial time species tree reconstruction from partially resolved gene trees. BMC Bioinformatics, 19(6):153, 2018. URL: https://doi.org/10.1186/s12859-018-2129-y, doi:10.1186/s12859-018-2129-y.
@article{10.1093/molbev/mst010,
author = "Katoh, Kazutaka and Standley, Daron M.",
title = "{MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability}",
journal = "Molecular Biology and Evolution",
volume = "30",
number = "4",
pages = "772-780",
year = "2013",
month = "01",
abstract = "{We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.}",
issn = "0737-4038",
doi = "10.1093/molbev/mst010",
url = "https://doi.org/10.1093/molbev/mst010",
eprint = "https://academic.oup.com/mbe/article-pdf/30/4/772/6420419/mst010.pdf"
}
@article{Emms2019,
author = "Emms, David M. and Kelly, Steven",
type = "Journal Article",
title = "OrthoFinder: phylogenetic orthology inference for comparative genomics",
journal = "Genome Biology",
number = "1",
doi = "10.1186/s13059-019-1832-y",
volume = "20",
pages = "238",
url = "https://doi.org/10.1186/s13059-019-1832-y",
year = "2019",
abstract = "Here, we present a major advance of the OrthoFinder method. This extends OrthoFinder’s high accuracy orthogroup inference to provide phylogenetic inference of orthologs, rooted gene trees, gene duplication events, the rooted species tree, and comparative genomics statistics. Each output is benchmarked on appropriate real or simulated datasets, and where comparable methods exist, OrthoFinder is equivalent to or outperforms these methods. Furthermore, OrthoFinder is the most accurate ortholog inference method on the Quest for Orthologs benchmark test. Finally, OrthoFinder’s comprehensive phylogenetic analysis is achieved with equivalent speed and scalability to the fastest, score-based heuristic methods. OrthoFinder is available at https://github.com/davidemms/OrthoFinder.",
isbn = "1474-760X",
DA = "2019/11/14"
}
@article{10.1371/journal.pbio.3001007,
author = "Steenwyk, Jacob L. and Buida, III, Thomas J. and Li, Yuanning and Shen, Xing-Xing and Rokas, Antonis",
doi = "10.1371/journal.pbio.3001007",
journal = "PLOS Biology",
publisher = "Public Library of Science",
title = "ClipKIT: A multiple sequence alignment trimming software for accurate phylogenomic inference",
year = "2020",
month = "12",
volume = "18",
url = "https://doi.org/10.1371/journal.pbio.3001007",
pages = "1-17",
abstract = "Highly divergent sites in multiple sequence alignments (MSAs), which can stem from erroneous inference of homology and saturation of substitutions, are thought to negatively impact phylogenetic inference. Thus, several different trimming strategies have been developed for identifying and removing these sites prior to phylogenetic inference. However, a recent study reported that doing so can worsen inference, underscoring the need for alternative alignment trimming strategies. Here, we introduce ClipKIT, an alignment trimming software that, rather than identifying and removing putatively phylogenetically uninformative sites, instead aims to identify and retain parsimony-informative sites, which are known to be phylogenetically informative. To test the efficacy of ClipKIT, we examined the accuracy and support of phylogenies inferred from 14 different alignment trimming strategies, including those implemented in ClipKIT, across nearly 140,000 alignments from a broad sampling of evolutionary histories. Phylogenies inferred from ClipKIT-trimmed alignments are accurate, robust, and time saving. Furthermore, ClipKIT consistently outperformed other trimming methods across diverse datasets, suggesting that strategies based on identifying and retaining parsimony-informative sites provide a robust framework for alignment trimming.",
number = "12"
}
@article{Steenwyk_2021,
author = "Steenwyk, Jacob L. and Buida, Thomas J. and Gon{\c{c}}alves, Carla and Goltz, Dayna C. and Morales, Grace and Mead, Matthew E. and LaBella, Abigail L. and Chavez, Christina M. and Schmitz, Jonathan E. and Hadjifrangiskou, Maria and Li, Yuanning and Rokas, Antonis",
doi = "10.1101/2021.10.02.462868",
url = "https://doi.org/10.1101\%2F2021.10.02.462868",
year = "2021",
month = "oct",
journal = "biorxiv",
publisher = "Cold Spring Harbor Laboratory",
title = "{BioKIT}: a versatile toolkit for processing and analyzing diverse types of sequence data"
}
@article{10.1093/molbev/msx281,
author = "Hoang, Diep Thi and Chernomor, Olga and von Haeseler, Arndt and Minh, Bui Quang and Vinh, Le Sy",
title = "{UFBoot2: Improving the Ultrafast Bootstrap Approximation}",
journal = "Molecular Biology and Evolution",
volume = "35",
number = "2",
pages = "518-522",
year = "2017",
month = "10",
abstract = "{The standard bootstrap (SBS), despite being computationally intensive, is widely used in maximum likelihood phylogenetic analyses. We recently proposed the ultrafast bootstrap approximation (UFBoot) to reduce computing time while achieving more unbiased branch supports than SBS under mild model violations. UFBoot has been steadily adopted as an efficient alternative to SBS and other bootstrap approaches. Here, we present UFBoot2, which substantially accelerates UFBoot and reduces the risk of overestimating branch supports due to polytomies or severe model violations. Additionally, UFBoot2 provides suitable bootstrap resampling strategies for phylogenomic data. UFBoot2 is 778 times (median) faster than SBS and 8.4 times (median) faster than RAxML rapid bootstrap on tested data sets. UFBoot2 is implemented in the IQ-TREE software package version 1.6 and freely available at http://www.iqtree.org.}",
issn = "0737-4038",
doi = "10.1093/molbev/msx281",
url = "https://doi.org/10.1093/molbev/msx281",
eprint = "https://academic.oup.com/mbe/article-pdf/35/2/518/24367824/msx281.pdf"
}
@article{10.1093/bioinformatics/btab096,
author = "Steenwyk, Jacob L and Buida, Thomas J, III and Labella, Abigail L and Li, Yuanning and Shen, Xing-Xing and Rokas, Antonis",
title = "{PhyKIT: a broadly applicable UNIX shell toolkit for processing and analyzing phylogenomic data}",
journal = "Bioinformatics",
volume = "37",
number = "16",
pages = "2325-2331",
year = "2021",
month = "02",
abstract = "{Diverse disciplines in biology process and analyze multiple sequence alignments (MSAs) and phylogenetic trees to evaluate their information content, infer evolutionary events and processes and predict gene function. However, automated processing of MSAs and trees remains a challenge due to the lack of a unified toolkit. To fill this gap, we introduce PhyKIT, a toolkit for the UNIX shell environment with 30 functions that process MSAs and trees, including but not limited to estimation of mutation rate, evaluation of sequence composition biases, calculation of the degree of violation of a molecular clock and collapsing bipartitions (internal branches) with low support.To demonstrate the utility of PhyKIT, we detail three use cases: (1) summarizing information content in MSAs and phylogenetic trees for diagnosing potential biases in sequence or tree data; (2) evaluating gene–gene covariation of evolutionary rates to identify functional relationships, including novel ones, among genes and (3) identify lack of resolution events or polytomies in phylogenetic trees, which are suggestive of rapid radiation events or lack of data. We anticipate PhyKIT will be useful for processing, examining and deriving biological meaning from increasingly large phylogenomic datasets.PhyKIT is freely available on GitHub (https://github.com/JLSteenwyk/PhyKIT), PyPi (https://pypi.org/project/phykit/) and the Anaconda Cloud (https://anaconda.org/JLSteenwyk/phykit) under the MIT license with extensive documentation and user tutorials (https://jlsteenwyk.com/PhyKIT).Supplementary data are available at Bioinformatics online.}",
issn = "1367-4803",
doi = "10.1093/bioinformatics/btab096",
url = "https://doi.org/10.1093/bioinformatics/btab096",
eprint = "https://academic.oup.com/bioinformatics/article-pdf/37/16/2325/39948152/btab096.pdf"
}
@article{eaton_toytree_2020,
author = "Eaton, Deren A. R.",
title = "Toytree: {A} minimalist tree visualization and manipulation library for {Python}",
volume = "11",
doi = "10.1111/2041-210X.13313",
journal = "Methods in Ecology and Evolution",
year = "2020",
pages = "187--191"
}
@article{10.1093/molbev/msaa015,
author = "Minh, Bui Quang and Schmidt, Heiko A and Chernomor, Olga and Schrempf, Dominik and Woodhams, Michael D and von Haeseler, Arndt and Lanfear, Robert",
title = "{IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era}",
journal = "Molecular Biology and Evolution",
volume = "37",
number = "5",
pages = "1530-1534",
year = "2020",
month = "02",
abstract = "{IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.}",
issn = "0737-4038",
doi = "10.1093/molbev/msaa015",
url = "https://doi.org/10.1093/molbev/msaa015",
eprint = "https://academic.oup.com/mbe/article-pdf/37/5/1530/33386032/msaa015.pdf"
}
@article{Kalyaanamoorthy2017,
author = "Kalyaanamoorthy, Subha and Minh, Bui Quang and Wong, Thomas K F and von Haeseler, Arndt and Jermiin, Lars S",
type = "Journal Article",
title = "ModelFinder: fast model selection for accurate phylogenetic estimates",
journal = "Nature Methods",
number = "6",
doi = "10.1038/nmeth.4285",
volume = "14",
pages = "587--589",
url = "https://doi.org/10.1038/nmeth.4285",
year = "2017",
abstract = "ModelFinder is a fast model-selection method that greatly improves the accuracy of phylogenetic estimates.",
issn = "1548-7105",
DA = "2017/06/01"
}
@article{Zhang2018,
author = "Zhang, Chao and Rabiee, Maryam and Sayyari, Erfan and Mirarab, Siavash",
type = "Journal Article",
title = "ASTRAL-III: polynomial time species tree reconstruction from partially resolved gene trees",
journal = "BMC Bioinformatics",
number = "6",
doi = "10.1186/s12859-018-2129-y",
volume = "19",
pages = "153",
url = "https://doi.org/10.1186/s12859-018-2129-y",
year = "2018",
abstract = "Evolutionary histories can be discordant across the genome, and such discordances need to be considered in reconstructing the species phylogeny. ASTRAL is one of the leading methods for inferring species trees from gene trees while accounting for gene tree discordance. ASTRAL uses dynamic programming to search for the tree that shares the maximum number of quartet topologies with input gene trees, restricting itself to a predefined set of bipartitions.",
issn = "1471-2105",
DA = "2018/05/08"
}
File(s) not Fasta or Genbank file. Suffix from file 'NC_026795-truncated.txt' is not Fasta or Genbank. File is assumed to be in Fasta format.